Publikationen

Zusammenfassung

Vacuum handling systems are widely used in production systems. Most of the systems key performance indicators (KPIs) relevant for industry 4.0 use cases can be derived from its vacuum level characteristics. Vacuum suction cups are key components of vacuum gripping systems and have a significant influence on the system behavior. For modelling suction cups, mainly constant volume chambers are currently used. However, these do not represent the suction cups dynamic behavior exactly enough. Therefore, a novel model is presented allowing the vacuum curve simulation mainly based on the force-displacement characteristic curve. The presented model is validated in an industrially relevant scenario.

Zusammenfassung

In many application domains data from different sources are increasingly available to thoroughly monitor and describe a system or device. Especially within the industrial automation domain, heterogeneous data and its analysis gain a lot of attention from research and industry, since it has the potential to improve or enable tasks like diagnostics, predictive maintenance, and condition monitoring. For data analysis, machine learning based approaches are mostly used in recent literature, as these algorithms allow us to learn complex correlations within the data. To analyze even heterogeneous data and gain benefits from it in an application, data from different sources need to be integrated, stored, and managed to apply machine learning algorithms. In a setting with heterogeneous data sources, the analysis algorithms should also be able to handle data source failures or newly added data sources. In addition, existing knowledge should be used to improve the machine learning based analysis or its training process. To find existing approaches for the machine learning based analysis of heterogeneous data in the industrial automation domain, this paper presents the result of a systematic literature review. The publications were reviewed, evaluated, and discussed concerning five requirements that are derived in this paper. We identified promising solutions and approaches and outlined open research challenges, which are not yet covered sufficiently in the literature.

Zusammenfassung

Vacuum handling systems are widely used in production systems. Most of the systems key performance indicators (KPIs) relevant for industry 4.0 use cases can be derived from its vacuum level characteristics. Vacuum suction cups are key components of vacuum gripping systems and have a significant influence on the system behavior. For modelling suction cups, mainly constant volume chambers are currently used. However, these do not represent the suction cups dynamic behavior exactly enough. Therefore, a novel model is presented allowing the vacuum curve simulation mainly based on the force-displacement characteristic curve. The presented model is validated in an industrially relevant scenario.

Zusammenfassung

With the increasing amount of available and connected data sources, industrial automation applications such as condition monitoring of a production machine can be improved by considering various data. To gain insights from this data and make it useable, heterogeneous data has to be analyzed intensively. Limited machine learning approaches exist in industrial automation and manufacturing for analyzing data acquired from multiple sources. In this paper, first, a suitable concept for handling heterogeneous data from integration to analysis is presented as well as a multi-layer architecture for the concept’s realization. The architecture encapsulates functionalities into the different layers and allows easy extendability and modifiability. Afterwards, a context modeling approach for managing heterogeneous data and existing approaches and algorithms for analyzing this data robustly and dynamically analyzing it are presented.

Zusammenfassung

A key performance indicator for sensors in production systems is their accuracy. In order to respond to the flexible requirements posed to production systems, there are currently two possibilities to realize the needed accuracy. Either the sensors can be changed frequently, so that the lowest still sufficient accuracy can always be achieved or the production system can be equipped directly with highly accurate sensors. These two options come with high costs, originating from the manual effort and the proportionality of accuracy and cost for most of the commonly used sensors. Industrially used resistance-based sensors such as pressure, force or temperature sensors represent a special case here, because their inaccuracy traces back to few major sources. To eliminate some of these sources or to decrease their impacts, instance-specific characteristics are used in an accuracy model provided by an intelligent Digital Twin. Using varying accuracy models provides different levels of accuracy. The presented concept provides the needed accuracy permanently or as a service for periods the production system needs the higher accuracy, called Accuracy-as-a-Service (AaaS), benefiting setup efficiency, costs and flexibility. To validate the presented concept, the proposed model is used for an analog pressure sensor in a fluidic test setup. The control unit of the system is provided with a standard model as well as a more accurate one. It is demonstrated in the paper how the usage of the individual model can improve the accuracy of a special sensor significantly by a factor of four.

Zusammenfassung

The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.

Zusammenfassung

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.

Zusammenfassung

The huge potential of the Digital Twin and its benefits in use cases such as virtual commissioning or reconfiguration planning for production systems is often described and well known. One reason that the usage of Digital Twins is becoming increasingly important are current trends, such as individualized mass production, which lead to more frequent reconfigurations of production systems or require higher flexibility. At the same time, many production systems have been in productive use for some time, so-called brownfield systems. Most of them do not have a Digital Twin, but would benefit from one, if available. However, the creation of a Digital Twin is a significant additional effort, due to the creation of the underlying models and the relations between them. Some promising approaches for the creation of domain-specific models already exist, which can be used for the creation of the unconnected parts of a Digital Twin. However, there are still few results for the creation of inter-domain relations, which cause problems in the creation of Digital Twins. This paper therefore presents a methodology for the automated reconstruction of functional relations as well as the separation of functionally related component groups of the plant and its software. For this purpose, the methodology imports the native PLC code via XML, extracts the relevant information from the code and enriches the resulting information model with explicit information about implicit relations in the code. Based on this, various elements of the PLC code are analyzed and the relationship of individual mechatronic components, that are available as signals, are examined and quantified. Thus, a part of the required relations for a Digital Twin is recognized automatically. The methodology and its benefits for the Digital Twin creation is presented and explained using a modular production system with several manufacturing stations as a proof-of-concept.

Zusammenfassung

Digital Twins, which serve as virtual representations, are becoming increasingly important to meet the needs of future industrial automation. Digital Twins are mostly created for specific use cases, facing various requirements e.g. reliable intellectual property protection, interoperability, flexibility, or extendability during implementation. For this purpose, decisions regarding structure and technology have to be made before implementation. In this paper, we discuss relevant requirements based on a proposed procedure for the derivation of project-specific intelligent Digital Twin implementations. The influences and correlations of project requirements are discussed concerning the choice of intelligent Digital Twins implementation structure and technology. In addition, the paper outlines their advantages and disadvantages. The proposed procedure is exemplarily illustrated using an intelligent Digital Twin utilized for a modular offshore-platform producing green hydrogen and derivatives.

Zusammenfassung

Behavior models in the Digital Twin are relevant to many use cases such as digital product development, virtual system optimization or virtual commissioning. However, such behavior models are structured in the literature according to different criteria and levels, which complicates the selection of the correct properties for specific use cases. One criterion that is discussed very differently is the modeling depth of behavior models. This paper presents different approaches to structure behavior models from literature and introduces a new concept with focus on the modeling depth. These levels of modeling depth, as well as the characteristics of the different modeling depths, are then illustrated using a representative industry scenario.

Zusammenfassung

A key performance indicator for sensors in production systems is their accuracy. In order to respond to the flexible requirements posed to production systems, there are currently two possibilities to realize the needed accuracy. Either the sensors can be changed frequently, so that the lowest still sufficient accuracy can always be achieved or the production system can be equipped directly with highly accurate sensors. These two options come with high costs, originating from the manual effort and the proportionality of accuracy and cost for most of the commonly used sensors. Industrially used resistance-based sensors such as pressure, force or temperature sensors represent a special case here, because their inaccuracy traces back to few major sources. To eliminate some of these sources or to decrease their impacts, instance-specific characteristics are used in an accuracy model provided by an intelligent Digital Twin. Using varying accuracy models provides different levels of accuracy. The presented concept provides the needed accuracy permanently or as a service for periods the production system needs the higher accuracy, called Accuracy-as-a-Service (AaaS), benefiting setup efficiency, costs and flexibility. To validate the presented concept, the proposed model is used for an analog pressure sensor in a fluidic test setup. The control unit of the system is provided with a standard model as well as a more accurate one. It is demonstrated in the paper how the usage of the individual model can improve the accuracy of a special sensor significantly by a factor of four.

Zusammenfassung

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.

Zusammenfassung

Neben Herausforderungen bei der Erstellung bieten sich Komponentenherstellern durch den Digitalen Zwilling viele potenzielle Mehrwerte. Auf die Anwendungsfälle, um die entsprechenden Mehrwerte zu realisieren, soll im Folgenden näher eingegangen werden. Für eine effiziente Erstellung aus Sicht des Komponentenherstellers wird ein Konzept präsentiert. Dieses nutzt bestehende Informationen und erstellt häufig fehlende Verhaltensmodelle automatisiert. Zur Veranschaulichung potenzieller Mehrwerte der Verhaltensmodelle wird der Luftverbrauch eines Vakuum-Greifsystems beispielhaft analysiert.

Zusammenfassung

Während der Engineering- und Betriebsphase modularer Produktionssysteme entstehen zunehmend Modelle. Der Digitale Zwilling kann diese Modelle für Anwendungen wie die virtuelle Inbetriebnahme, Optimierung, Prognose oder betriebsparallele Simulation zur Verfügung stellen. Durch unvorhersehbare Veränderungen in der Betriebsphase verschlechtert sich die Realitätsnähe der Modelle des Digitalen Zwillings, bspw. aufgrund von Anforderungsänderungen, neuen Anwendungen oder sich ändernden Umwelteinflüssen wie etwa Temperatur- oder Druckschwankungen und eine Modelladaption wird notwendig. Die Adaption der Modelle des Digitalen Zwillings weist einige Herausforderungen auf, die in diesem Beitrag aufgezeigt werden. Um diesen Herausforderungen zu begegnen und dem Adaptionsaufwand während der Betriebsphase entgegenzuwirken, wird ein Konzept präsentiert, welches es dem Digitalen Zwilling ermöglicht, die Abweichung zur Realität zu erkennen und sich durch eine anschließende Modelladaption realitätsnah zu halten.

Zusammenfassung

The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI.

Zusammenfassung

This paper presents an approach to combine multiple temperature-sensitive electrical parameters to improve the accuracy and precision of the junction temperature estimation of power transistors using the example of a silicon-carbide power MOSFET. Switching delays and the threshold voltage of the power transistor during turn-on and -off of a silicon-carbide power transistor are used as temperature-sensitive electrical parameters for the online junction temperature measurements. In order to improve the accuracy, a shallow fully-connected neural network is used as the means to combine the four measurements in one switching cycle of the transistor. The maximum measurement error of the junction temperature of the power transistor is reduced approximately 10-fold from 8.98 K to 0.92 K.

Zusammenfassung

Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.

Zusammenfassung

Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.

Zusammenfassung

Die Produktion von Wasserstoff und Folgeprodukten unter Nutzung erneuerbarer Energien gilt als großer Hoffnungsträger für das Erreichen einer Klimaneutralität. Im Rahmen des Leitprojekts H2Mare soll der Betrieb einer Offshore-Plattform für Power-to-X-Produktionsprozesse erforscht und erprobt werden. Im vorliegenden Beitrag wird zunächst das Projektkonzept einer modularen Offshore-Plattform skizziert. Anschließend werden mit dem Digitalen Zwilling und dem System zur optimalen Betriebsführung zwei Forschungsansätze präsentiert, die zur Effizienzsteigerung dieser Produktionsprozesse beitragen sollen.

Zusammenfassung

The use of Digital Twins is versatile and offers a wide range of advantages. However, the creation is often a manual process and therefore very time-consuming and cost-intensive. The creation of behavior models stands out as particularly time-consuming. Therefore, this paper deals with the creation of Digital Twins from the perspective of an industrial component manufacturer, with a particular focus on the creation of behavior models. An automatic approach based on libraries is presented. This concept, in particular for the creation of behavior models, is validated in an industry-relevant application scenario and compared with the manual creation process.

Zusammenfassung

The huge potential of the Digital Twin and its benefits in use cases such as virtual commissioning or reconfiguration planning for production systems is often described and well known. One reason that the usage of Digital Twins is becoming increasingly important are current trends, such as individualized mass production, which lead to more frequent reconfigurations of production systems or require higher flexibility. At the same time, many production systems have been in productive use for some time, so-called brownfield systems. Most of them do not have a Digital Twin, but would benefit from one, if available. However, the creation of a Digital Twin is a significant additional effort, due to the creation of the underlying models and the relations between them. Some promising approaches for the creation of domain-specific models already exist, which can be used for the creation of the unconnected parts of a Digital Twin. However, there are still few results for the creation of inter-domain relations, which cause problems in the creation of Digital Twins. This paper therefore presents a methodology for the automated reconstruction of functional relations as well as the separation of functionally related component groups of the plant and its software. For this purpose, the methodology imports the native PLC code via XML, extracts the relevant information from the code and enriches the resulting information model with explicit information about implicit relations in the code. Based on this, various elements of the PLC code are analyzed and the relationship of individual mechatronic components, that are available as signals, are examined and quantified. Thus, a part of the required relations for a Digital Twin is recognized automatically. The methodology and its benefits for the Digital Twin creation is presented and explained using a modular production system with several manufacturing stations as a proof-of-concept.

Zusammenfassung

In response to the demographic change and the accompanying challenges for effective healthcare, approaches to enable using advancements of digitalization and IoT infrastructures as well as AI methods to deliver results in the field of personalized health assistance are necessary. In our research, we aim at enabling user-centered assistance with the help of networked sensors and Health Assistance Systems as well as learning methods based on connected graph data that model the shared system, user, and environmental context. In particular, this paper demonstrates a graph-based dynamic context model for a medication assistance system and presents an association rule learning method using Apriori algorithm to learn correlations between user vitals, activities as well as medication intake behavior. An application scenario for context-based heart rate monitoring is consequently presented as proof of concept, where associated contextual elements from the modeled context relating surges in monitored heart rate to environmental and user activity are shown.

Zusammenfassung

Trotz hoher Lösungspotentiale des maschinellen Lernens für gängige Probleme der Automatisierungstechnik, finden sich in der Praxis wenig Anwendungsbeispiele. Um der Ursache hierfür auf den Grund zu gehen, zeigen die Autoren anhand von vier beispielhaften Anwendungsfällen die Hürden für konventionelles maschinelles Lernen auf und benennen Lösungsansätze mittels industriellem Transfer-Lernen. Zum großflächigen Einsatz derartiger Ansätze, fehlt es an Voraussetzungen, deren Schaffung jedoch anders als beim konventionellen maschinellen Lernen grundsätzlich möglich ist. Der Artikel schließt mit einer Betrachtung dieser Voraussetzungen und macht Vorschläge, wie sie zu erfüllen sind.

Zusammenfassung

Despite the high solution potential of machine learning for common problems in automation technology, there are only few examples of its application in real-world manufacturing practice. In order to find the reason for this phenomenon, the authors identify the hurdles for conventional machine learning using four exemplary use cases namely self-learning robots, wear prediction, visual object detection, and predictive quality in manufacturing. While these use-cases differ in principle, the problems engineers face when using conventional machine learning approaches to solve them are related, such as the lack of manifold training data or high dynamics of industrial processes. The authors showcase that utilizing deep transfer learning and continual learning approaches in the industrial context – subsumed under the term industrial transfer learning – can overcome these hurdles. Even for industrial transfer learning, there is a deficiency regarding preconditions for the large-scale deployment of such approaches, but unlike in conventional machine learning, it is principally possible to establish those. The article concludes with a discussion of these prerequisites and makes suggestions as to how they could be fulfilled.

Zusammenfassung

The concept of the intelligent Digital Twin focuses on automatic model creation and model extension for a product throughout its lifecycle using artificial intelligence. To create an intelligent Digital Twin, the Digital Twin architecture must also include software services that support machine learning algorithms. These software services must have two characteristics. First, an interface to the real environment to dynamically receive feedback from an existing product or a product under development. Second, machine learning algorithms that analyze and manage the Digital Twin's models based on feedback from the real product. In this paper, we present the concept and implementation of a software service within the intelligent Digital Twin's architecture for automated requirements modeling as well as for modeling of the product system architecture. Since medical devices differ significantly in their applied technology, area of use, and application, the requirement engineering of medical devices needs to identify and specify the critical design requirements for each product individually. This makes automated requirement engineering and Model-based-Systems Engineering of medical devices very challenging. The implemented intelligent software service meets these challenges for product design in the healthcare sector.

Zusammenfassung

Autonomous systems gain more and more interest in research and society. However, they bring new challenges in safeguarding these systems. This contribution orders those new challenges and provides an overview of already existing concepts and approaches to solve those challenges of safeguarding autonomous systems. Moreover, existing metrics for safeguarding of autonomous systems are systematically reviewed. The presented concepts and approaches are of different domains, namely, ground, nautical and aerial vehicles, industrial robots and smart manufacturing, and medical and healthcare. Finally, the concepts and approaches are discussed concerning the following points: Main ideas, parallels existing in different domains, which ideas can be transferred from one domain to another, which high-level tasks were adressed and which assumptions were made.

Zusammenfassung

This paper presents a novel approach to ensure the quality of the Digital Twin models that modern Cyber-Physical Manufacturing Systems (CPMS) rely on. CPMS are configurable and intelligent. Environmental and system parameters change frequently. Thus, static models are inadequate. Autonomous mobile robots and the simulation of their movement are important elements of these CPMS. Based on our reinforcement learning-based methodology, we use these robots as an example to show how the Digital Twin automatically improves models that do not perfectly represent the physical asset, making it an intelligent Digital Twin. In our scenario, the behavior of the asset deviates from the simulated prediction, i.e., a simulation gap occurs. The presented approach closes this simulation gap through a three-step mechanism. First, it makes the simulated data and the real data comparable and synchronizes it. Second, it applies reinforcement learning to find patterns in the deviations between the simulated and real data. Third, it learns to compensate for them. The evaluation of this example shows promising results.

Zusammenfassung

Increasing productivity, as well as flexibility, is required for the industrial production sector. To meet these challenges, concepts in the field of “Industry 4.0” are arising, such as the concept of Digital Twins. Vacuum handling systems are a widespread technology for material handling in industry and face the same challenges and opportunities. In this field, a key issue is the lack of Digital Twins containing behavior models for vacuum handling systems and their components in different applications and use cases. Therefore, the object of this paper is to propose a novel concept for modeling and simulating the fluidic behavior of pneumatic vacuum ejectors as key components of vacuum handling systems. In order to increase the simulation accuracy, the concept can access instance‐specific data of the used asset instead of object‐specific data. The model and the data are part of the Digital Twins of pneumatic vacuum ejectors, which shall be able to be combined with other components to represent a Digital Twin of entire vacuum handling systems. The proposed model is validated in an experimental test set‐up and in an industrial application delivering sufficiently accurate results.

Zusammenfassung

Failure analysis is essential for improving the reliability and manufacturability of electronic devices. With the time-domain reflectometry method, failures can be analyzed non-destructively. The method enables the detection, location, and characterization of hard interconnection failures (open or shorts) as well as of soft interconnection failures, which can give an outlook on imminent hard failures. Generating measurement data from real failed devices is costly since failed devices need to be selected and the measurements need to be performed and prepared. In contrast, simulation models are often available where all possible kinds of failures can be created. Therefore, we propose simulation to real transfer learning for the failure analysis on time-domain reflectometry data. A deep learning model shall first be trained on time-domain reflectometry simulation data and then be transferred to measurement data of a power transistor. We investigate different possibilities of transfer and evaluate the performance of a SiC power transistor.

Zusammenfassung

In the production of the future, people and robots will work together in dynamic environments. Industrial mobile robots drive past workers and obstacles to the assemblers' workstations, pick up workpieces and deliver them to the next free workstation. To ensure reliable production, robots must carefully consider their actions based on the current situation to minimize losses. In order to reason about losses, it is necessary to identify loss scenarios that are likely to arise from the system's situation. In this paper, the authors propose a novel methodology for identifying these likely loss scenarios based on fault injectors. The fault injectors are trained using reinforcement learning. Based on a probability model of disruptive events (faults), the agents learn to destabilize the system by simulating such events. When a chain of these events leads to a loss, a loss scenario is found. The higher the probability of the loss scenario, the higher the reward for the agent. In this way, the fault injectors optimize for maximally likely loss scenarios. After describing the methodology, the authors propose a framework for systematically classifying different types of fault injectors and give advice on how to build them. Finally, the authors demonstrate reinforcement learning-based fault injectors on the path planning of the mobile robot platform Robotino.

Zusammenfassung

The emerging autonomous mobile robots promise a new level of efficiency and flexibility. However, because these types of systems operate in the same space as humans, mobile robots must cope with dynamic changes and heterogeneously structured environments. To ensure safety, new approaches are needed that model risk at runtime. This risk depends on the situation and is therefore a situational risk. In this paper, we propose a new methodology to model situational risk based on multi-agent adversarial reinforcement learning. In this methodology, two competing groups of reinforcement learning agents, namely the protagonists and the adversaries, fight against each other in the simulation. The adversaries represent the disruptive and destabilizing factors, while the protagonists try to compensate for them. The situational risk is then derived from the outcome of the simulated struggle. At this point, the system’s Digital Twin provides up-to-date and relevant models for simulation and synchronizes the simulation with the real asset. Our risk modeling differentiates the four steps of intelligent information processing: sense, analyze, process, and execute. To find the appropriate adversaries and actors for each of these steps, this methodology builds on Systems Theoretic Process Analysis (STPA). Using STPA, we identify critical signals that lead to losses when a disturbance under certain conditions or in certain situations occurs. At this point, the challenge of managing the complexity arises. We face this issue using training effort as a metric to evaluate it. Through statistical analysis of the identified signals, we derive a procedure for defining action spaces and rewards for the agents in question. We validate the methodology using the example of a Robotino 3 Premium from Festo, an autonomous mobile robot.

Zusammenfassung

Vision applications are becoming increasingly important for product quality surveillance in manufacturing. Training consistently well-performing visual detection algorithms based on convolutional neural networks is very challenging. Typically, there is too much training data for engineers to keep track of possible gaps in it. But even small cases of missing training data e.g. certain viewing angles can lead to trained CNNs that are unable to detect objects, that seem obvious to engineers i.e. cognition gaps. This paper presents how synthetic training data can be created in a targeted manner to close cognitive gaps of a CNN for specific use-cases. The proposed methodology uses 3D rendering to create new image data by variating scene parameters. The created data is used to reveal a cognition gap of a CNN. We show that by using this created synthetic data to train the CNN the cognition gap can be successfully closed. This is evaluated with the well-known AlexNet CNN used as a visual bicycle detector. The bicycle example is used as a stand-in for a geometrically interesting, but simple product, that is manufactured in large and growing amounts.

Zusammenfassung

Testing is often considered boring. Wrong! Testing matters for many reasons. Not only is it the area where most of the money goes across the product life cycle but new challanges, such as adaptive software, agile deployment, and artificial intelligence (AI)-driven systems, demand bnovel test technologies. Michael Weyrich, Divith Bajaj, and I evaluate current technologies for software testing, providing an overview of methods and tools, including systematic risk assesment for each test activity. A case study for testing AI systems provides insight into novel intelligent testing stratigies.

Zusammenfassung

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.

Zusammenfassung

Industrial mobile robots increasingly operate in dynamic and complex environments, resulting in risks that arise from the situation. Consequently, mobile robots need knowledge about the situative risk to make responsible decisions, e.g., when planning a trajectory. In this paper, the authors present a new approach to evaluate these situative risks at runtime. To compute the situative risk, the proposed approach uses multi-agent adversarial reinforcement learning. Unlike the usual approach that uses the self-play mechanism to harden the agents against disturbances, the approach directly uses the self-play to determine the confidence that the system can handle the current situation. Based on the evaluation of the strength of the perceived perturbations, the robot's digital twin calibrates the action space of the adversarial agents. In this way, the adversarial agents represent the disturbances that are likely to occur. From the simulation results of the competing agents, the digital twin infers the confidence with which the system can handle the situation. In the event that the system cannot handle the situation and crashes in the simulation, the digital twin calculates the probability that the simulated scenario will occur in reality. To calculate this probability, the authors propose the method of game-theoretic event trees. Combining the probability of an accident scenario with the damage caused by the simulated hazard, the results of situative risk are obtained. The approach is qualitatively evaluated using a case study. In this case study, a industrial mobile robot called Robotino is considered that connects workstations in a matrix production system. The case study shows that the approach is promising.

Zusammenfassung

In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.

Zusammenfassung

In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.

Zusammenfassung

The use of the Digital Twin as a promising technology during the reconfiguration of automated systems enables to meet the challenges of increasing product diversity and shortening product life cycles in today's industry. The use of Digital Twins supports engineers in all phases of the life cycle of a production line. Over time, however, production facilities are often modified and improved, while at the same time the created models during the engineering process of the system remain unchanged and no longer correspond to the real facility. Manual updating of the positions in the digital layout is very time-consuming and therefore expensive. This paper presents a concept for the automatic update of the layout of a production line. In this concept, the positions of the robots and their active and passive peripheral devices are automatically positioned in a digital plant model using information from the current configuration of the robots in a production line. Therefore, engineers can use the resulting synchronized digital plant model, Digital Twin of the system, to further optimize or expand the production plant.

Zusammenfassung

This paper presents a study about a runtime mechanism to monitor the performance degradation in intra-vehicular networks. The proposed mechanism focuses on the integration of fault modeling in communication protocols, as non-functional requirements (NFR), using aspect-oriented modeling (AOM), to model the performance degradation generated by faults, linking test and design phases of distributed control systems. A case study analyzing the mechanism performance in both CAN and CANFD protocols was conducted considering the NFR specification related to fault disturbances. In order to evaluate and simulate the mechanism under real fault scenarios, an active suspension control system was considered as an example of a critical control system and faults were injected using the hardware Vector VH6501 (CAN disturbance interface). The network performance analysis was made based on the software Vector CANoe considering different network busloads and CAN/CAN-FD rates between 1 to 4 Mbps. Results show that the mechanism efficiently detects anomalous events on performance with short response time with busloads up to 30%. The performed experiments show better performance on CAN-FD registering lower average jitter during fault injection in all busloads tested scenarios.

Zusammenfassung

Electronic devices are one of the key factors for recent advances in smart production systems or automotive. Reliability and robustness are key issues. To further increase this reliability, occurring failures in an electronic device has to be investigated in post-production failure analysis processes. One recent technique to detect and locate failures in electronic components is Time-Domain Reflectometry. This method offers the chance to detect several kinds of failures (e.g. a hard or soft failure) and localize the failure nondestructively. In theory, this can be determined following defined physical formulas. Nevertheless, the received signals are not perfect and mixed with noise from the measurement device or disturbed by nonoptimal material properties. In addition, complex architectures of devices are hard to model based on analytical models. Thus, these models solely are not sufficient for the failure analysis process. For this reason, a hybrid modeling approach is proposed, using a Machine Learning model in combination with physical models to detect and characterize the failure and its exact position. The Machine Learning model will be trained with simulated Time-Domain Reflectometry data.

Zusammenfassung

Developing consistently well performing visual recognition applications based on convolutional neural networks, e.g. for autonomous driving, is very challenging. One of the obstacles during the development is the opaqueness of their cognitive behaviour. A considerable amount of literature has been published which describes irrational behaviour of trained CNNs showcasing gaps in their cognition. In this paper, a methodology is presented that creates worst-case images using image augmentation techniques. If the CNN's cognitive performance on such images is weak while the augmentation techniques are supposedly harmless, a potential gap in the cognition has been found. The presented worst-case image generator is using adversarial search approaches to efficiently identify the most challenging (worst) image. This is evaluated with the well-known AlexNet CNN using images depicting a typical driving scenario.

Zusammenfassung

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more com-plex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes.

Zusammenfassung

Digital Twins are being increasingly used in manufacturing industry to support the whole plant lifecycle. The constantly changing environmental parameters, interactions between various systems and their Digital Twin as well as changes inside the Digital Twin influences the context of the available information. This context information is insufficiently considered to analyze process and optimize the interaction. This article presents an approach to model the internal and external context of a system using a graph-based context tier model. The usage and benefits.

Zusammenfassung

The progress of digitalization and Internet of Things enables more and more complex, networked and powerful Cyber-physical Systems (CPS) operating in uncertain environments. This complexity and uncertainty, however, makes it unfeasible to model every aspect in advance. This causes the models to leave their scope and reach their capability limits. Specifically, in safe maintenance planning for highly-automated trucks, this fact causes waste of valuable resource, since maintenance models are often more rule-of-thumb (e.g. operation hours) than precise. In order to counteract this issue, we propose extending the digital twin concept by artificial intelligence such that the models become dynamic and adaptive. Having described the general approach and its architecture, we showcase and evaluate the approach in a highly-automated truck scenario.

Zusammenfassung

Future automation systems, operating under increasing dynamic conditions need to be equipped with methods to enhance their adaptability accordingly. Context-aware systems model and apply contextual parameters influencing the interpretation of situations to adapt their operation accordingly. Designing context-aware automation systems requires methods to acquire and process heterogeneous and distributed data in a dynamic and continuous manner. In this contribution, a methodology for processing multimodal data in a flexible and reusable context-model is demonstrated, using concepts of data aggregation and integration, semantic labeling and graph modeling. Based on this approach, the use case of a context-aware medication assistance system is subsequently presented.

Zusammenfassung

Safety and reliability are two critical factors of modern Cyber-Physical Systems (CPS). However, the increasing structural and behavioral complexity of modern automation systems significantly increases the possibility of system errors and failures, which can easily lead to economic loss or even hazardous events. Anomaly Detection (AD) techniques provide a potential solution to this problem, and conventional methods, e.g., Autoregressive Integrated Moving Average model (ARIMA), are no longer the best choice for anomaly detection for modern complex CPS. Recently, Deep Learning (DL) and Machine Learning (ML) anomaly detection methods became more popular, and numerous practical applications have been presented in many industrial scenarios. Most of the modern DL-based anomaly detection methods use the prediction approach and LSTM architecture. The Transformer is a new neural network architecture that outperforms LSTM in natural language processing. In this paper, we show that the Transformer-based deep learning model, which has received much attention recently, can be applied to the anomaly detection of industrial automation systems. Specifically, we collect time-series data from a system of two industrial robots using a Simulink model. Then, we feed these data into our Transformer-based model and train it to be a time-series data predictor. The paper presents the experimental results that show the comparison of precision and speed of a Long-Short Time Memory (LSTM) predictor and our Transformer-based predictor.

Zusammenfassung

At collaborative workspaces, humans and robots share the shop floor and work closely together. Operator 4.0 is a wide research topic and its solutions aim at the creation of Human-centered Cyber-Physical Systems that improve operators’ capabilities. Such applications require a bi directional flow of information and need data, models and simulations of machines as well as humans. To realize a common interface for information, the concept of Digital Twin is promising. This paper therefore discusses the adaption of conventional Digital Twin architectures and presents a derived Human-centered Digital Twin (H-DT) architecture designed for operators in production and intralogistics.

Zusammenfassung

The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned, supported and simplified with the Digital Twin of the system providing this knowledge. However, digital models as the basis of a Digital Twin are usually missing for these plants. This article presents a data-driven approach to gain knowledge about a brownfield system to create the digital models of a Digital Twin and their relations. Finally, a proof of concept shows that process data and position data as data sources deliver the relations between the models of the Digital Twin.

Zusammenfassung

Im Sinne der Qualitätssicherung sollen zukünftig produzierte Halbleiterbauelemente den steigenden Anforderungen an Zuverlässigkeit, Konnektivität und Automatisierung standhalten. Dazu ist ein Fehleranalyseprozess notwendig. Um die Effizienz zu steigern und die Fehleranfälligkeit zu reduzieren, soll der Fehleranalyseprozesses mit dazugehöriger Datenanalyse automatisiert werden. Dafür muss zunächst ein Konzept für die automatisierte Integration heterogener Daten aus unterschiedlichen Datenquellen auf Basis aktueller Daten Engineering Methoden entwickelt werden. Die Daten aus unterschiedlichen Quellen (z.B. Produktionsdaten, Datenblätter, Analyseverfahren) müssen eindeutig einem Bauelement zugeordnet werden können und neue Daten oder Analyseergebnisse diesem hinzugefügt werden können. In diesem Beitrag wird für die automatisierte Datenintegration des Fehleranalyseprozesses von Halbleiterbauelementen ein Konzept mithilfe von Ontologien und Graphen vorgestellt und dessen Mehrwerte anhand eines Anwendungsfalls mit einer prototypischen Umsetzung diskutiert. Dafür wird ein hybrider Ontologie-Ansatz genutzt, um die heterogenen Daten in einem Knowledge Graphen zu verknüpfen. Der vorgestellte Ansatz ermöglicht sowohl eine lokale als auch eine globale Datenanalyse. Dadurch können sowohl bisher entwickelte Datenanalyse-Ansätze genutzt werden, als euch neue Algorithmen aufgebaut werden.

Zusammenfassung

The frequency of changes in production requirements is continuously increasing due to economic volatility, shorter innovation cycles and product life cycles. Therefore, a prediction of all goals of a production system at development time is impossible, which is leading to an increased reconfiguration demand during operation. Currently, there are still some weaknesses concerning the reconfiguration of production systems, which are highlighted in this article. The future of industrial automation will be dominated by Cyber-Physical Production Systems, which offer many promising potentials. Hence, this contribution discusses the Cyber-Physical Production Systems and some of their potentials regarding the reconfiguration issues. Corresponding concepts are required to utilize these theoretical potentials. Therefore, this research provides a basic concept for self-organized reconfiguration management.

Zusammenfassung

Data-science-driven development projects are increasingly gaining the attention of small and medium sized enterprises. Since SME are often lacking the necessary competencies in data science, cooperation with other companies or universities is required. The efficient handling of data is one of the main challenges in joint cross-enterprise development projects. Actual cost driver is the development of data by labeling and classifying the data by domain experts, which is very time-consuming and labor-intensive with large amounts of data. Furthermore, clearance processes also have a high potential to cause delays before data can be shared with project partners. Moreover, before the actual work can begin, it is often necessary to clean up and repair incomplete or noisy data. The concept of Data Administration Shell presented in this paper addresses the challenge of structured information sharing and information management in joint cross-enterprise engineering. The Data Administration Shell links data sets to information regarding data origin and already performed analyses including their results and program scripts. Adding relations and documentation facilitates the reuse of data sets for subsequent projects. For this purpose, the Data Administration Shell adapts the concepts serving the information sharing in the research field of manufacturing and Digital Twin. The evaluation of the Data Administration Shell was based on time-series measurement data from a production process optimization scenario. Here, the Data Administration Shell manages the data sets of time series data and facilitates the joint cross-enterprise engineering of data-driven solutions.

Zusammenfassung

Increased flexibility and improved resilience in production and manufacturing processes are goals that are becoming more and more important in the context of Industry 4.0 and from the experience of the Covid-19 pandemic. At the same time, efficient operation of production must be guaranteed to achieve economic as well as ecological objectives. Intelligent assistance systems follow the idea to support stakeholders of production systems in their decisions and can thus be useful applications for helping to master the various challenges and to meet these goals. In this paper, we describe functional components of these decision support systems: data provision and data extraction, knowledge base, simulation models, model execution and analytics, and application and user interaction. We show the underlying technologies and illustrate why these assistant systems are valuable for several stakeholders.

Zusammenfassung

The utilization of deep learning in the field of industrial automation is hindered by two factors: The amount and diversity of training data needed as well as the need to continuously retrain as the use case changes over time. Both problems can be addressed by industrial deep transfer learning allowing for the performant, continuous and potentially distributed training on small, dispersed datasets. As a specific example, a dual memory algorithm for computer vision problems is developed and evaluated. It shows the potential for state-of-the-art performance while being trained only on fractions of the complete ImageNet dataset at multiple locations at once.

Zusammenfassung

Recently we introduced a new model-based fault injection method implemented as a highly-customizable Simulink block called FIBlock. It supports the injection of typical faults of essential heterogeneous components of Cyber-Physical Systems, such as sensors, computing hardware, and network. The FIBlock allows to tune a fault type and configure multiple parameters to tune error magnitude, fault activation time, and fault exposure duration. We demonstrated its performance to generate highly adjustable and various CPS fault types on a Simulink case study representing a lower-limb EXO-LEGS exoskeleton, an assistive device for the elderly in everyday life. In our previous paper we discovered the spatial and temporal thresholds for different fault types. Upon exceeding said thresholds, the Dynamic Movement Primitives-based control system could no longer adequately compensate these errors. In this paper we proposed a Deep Learning-based approach for the system failure prevention. We employed the Long Short-Term Memory (LSTM) network for error detection and mitigation. Error detection was performed using data prediction. The LSTM models were mitigating the detected errors with computed predictions only when they were subject to the imminent failure (i.e., exceeded the aforementioned thresholds). To compare our approach with previous findings, we trained two LSTM models on angular position and on angular velocity signals. For evaluation, we performed fault injection experiments with varying fault effect parameters. The ’Stuck-at current value’ fault was injected into angular position sensor, and the ’Stuck-at 0’ fault was injected into angular velocity sensor. Presented Deep Learning-based approach prevented system failure even when the injected faults were substantially exceeding thresholds. In addition, a reasoning for data access point choice has been evaluated. We compared two options: (i) the input data for LSTM is provided from the controller output and (ii) from the sensor output. In the paper the pros and cons for both options are presented. We deployed the trained LSTM models on an Edge Tensor Processing Unit. For that, the models have been quantized, i.e. all the 32-bit floating-point numbers (such as weights and activation outputs) were converted to the nearest 8-bit fixed-point numbers and converted to the TensorFlow Lite models. The Coral USB Accelerator was coupled with a Raspberry Pi 4B for signal processing. The result proves the feasibility of the proposed method. Because the LSTM models were converted to the 8-bit integer TensorFlow Lite models, it allowed real-time error mitigation. Furthermore, the light weight of the system and a minimal power consumption allows its integration into wearable robotic systems.

Zusammenfassung

In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector’s requirements. To better describe these requirements, base use cases of industrial transfer learning are introduced.

Zusammenfassung

Der Digitale Zwillinge einer Produktionsanlage kann für viele Use Cases sehr hilfreich sein. Dazu muss die Synchronität des Digitalen Zwillings mit dem realen Produktionssystem sichergestellt werden. Diese herausfordernde Aufgabe kann durch unterschiedliche Methoden unterstützt oder (teil-) automatisiert werden. Ein wichtiger Aspekt, der für Digitale Zwillinge beachtet werden muss, sind die Relationen zwischen den einzelnen Modellen der Dimensionen Mechanik, Elektrik und Software. Daher werden die gängigen Methoden zur Synchronisierung und Erstellung digitaler Modelle aus multiplen Dimensionen vorgestellt und die Relationsbildung betrachtet. Die Ankerpunktmethode als vielversprechendste Methode wird anschließen an einer Beispielsanlage untersucht und deren Grenze aufgezeigt. Basierend auf diesen Ergebnissen wird eine Methodik vorgestellt, die sich mit der Rekonstruktion der digitalen Modelle und deren Relationen beschäftigt. Dabei liegt der Fokus auf bereits existierenden, produktiv eingesetzten Produktionssystemen, sogenannte Brownfield Anlagen, als das häufigste Einsatzfeld.

Zusammenfassung

Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively.

Zusammenfassung

Model-based systems engineering is a methodology for the interdisciplinary system development using different domain models. Considering the intended system’s environment and context of usage in early design phases is a way of bridging the gap from system’s design to its real-life applicability. Simulations represent a possible approach to consider different constellations but are effortful. Therefore, we present a generic environment modeling approach to evaluate different environmental models with their respective environmental, user and system’s impact based on generated system variants derived from system requirements. In this sequential process, we consistently link and evaluate system variants to possible system contexts.

Zusammenfassung

Ensuring and improving the reliability of electronic devices requires post-production failure analysis processes. One of the techniques to perform failure analysis for electronic devices is Time-Domain Reflectometry. With this method, failures can be detected, located, and characterized non-destructively. It enables not only the detection of hard interconnection failures (such as an open or a short) but also the detection and characterization of soft failures. For Time-Domain Reflectometry, known physical equations can be applied to model the ideal behavior of a signal for different failures. However, since electronic device architectures and thus the data become more and more complex and measurements are disturbed by noise or nonoptimal material properties, these models solely are not sufficient for failure analysis. Therefore we propose hybrid modeling, where machine learning models (e.g. convolutional neural networks) work together with physical models to detect, locate, classify and characterize the failure. The first approach is shown on simulated microstrip line data and then transferred to SiC transistors for evaluation.

Zusammenfassung

Ensuring high availability despite the growing frequency of changes in production requirements leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by Cyber-Physical Production Systems (CPPS) in the future. Therefore, a concept, covering a methodology containing four steps, is utilized to answer the research question how to enhance CPPS with a self-organized reconfiguration management. This contribution focuses on the first two steps of this concept: the identification of reconfiguration demand and the generation of alternative configurations. A condensed presentation of the related work reveals that an interface-oriented formalized process description depicts an appropriate conceptual basis. Building upon this, the presented concept contains a capability model of the CPPS and the reasoning to determine a potential reconfiguration demand, as well as a procedure for the generation of alternative configurations. To evaluate the concept, an agent-based implementation is given, which uses an OPC-UA controlled simulated modular production system as a substitute CPPS in discrete manufacturing.

Zusammenfassung

Advances in the domain of industrial automation systems and artificial intelligence research have brought autonomous systems into the focus of industrial automation research. However, as Hrabia et al. state, there is no common understanding of what autonomous systems are and what characterizes them: “there is still a lack of a widely accepted concept of autonomous systems or robots that contains a detailed definition according to different aspects and a clear distinction to other system concepts” 1. They conclude “the capability of adapting to the environment is strongly interconnected with autonomy”1. However, they do not derive a comprehensive definition. On the one hand, some systems are advertised as autonomous systems, although they are only highly automated systems. On the other hand, some autonomous systems are not labeled as such. In addition, a number of different properties have emerged that characterize an autonomous system as well as abilities attributed to autonomous systems. In order to provide a differentiated view on this topic, the relevant literature in the domain of industrial automation systems was investigated for this survey. The methodology of the investigation is described in Section 2. Starting with the historical development of the term autonomy, in Section 3, various definitions of autonomy are compared, and a summarizing definition is elaborated. From the definition in the narrow sense, the characteristics of industrial autonomous systems are derived (Section 3.1) and a distinction from intelligent industrial automation systems and classic automation systems is proposed (Section 3.2). Reviewing the abilities attributed to industrial autonomous systems in the literature, the abilities are categorized according to the characteristics (Section 4). In order to provide an example for the classification of systems as industrial autonomous systems based on the four derived characteristics, their manifestation is explored by three real-world examples (Section 5). Finally, Section 6 provides a conclusion and an outlook.

Zusammenfassung

Digital Twins and the Internet-of -Things (IoT) are going to boost many applications based on information and communication technology. A study of COOM19 indicates that the technology of a Digital Twin is about to be established in Industry. It was found that “62 percent (of companies questioned) are either in the process of establishing Digital Twin use or plans to do so”. Many authors agree that these new technologies will connect virtually everything from customers to machines and logistics. AutomationML is an important standard and relevant enabler for the creation, adaption and management of the Digital Twin. In the near future, it is expected that machines will communicate with one another, logistics and machinery will be self-controlled and all plans can be projected by simulation, which supports decision-making. From this perspective, the Digital Twin is a novel and enabling technology, which can be spread easier and faster with the use of AML. The demands of markets to release constantly market-driven innovations, compels industrial manufacturing companies to increase both the use of automated production systems and their reconfiguration during their lifecycle. Increasing product variety and shortening product life cycles require a fast and inexpensive reconfiguration of existing production systems AJS+18a. To face these challenges, one solution is to use the Digital Twins of automated manufacturing systems. The value-add of a Digital Twin for reconfiguration, promises the reconfiguration time to be reduced and thus an increase of the system availability as well as customer-specific products to be manufactured at short notice. A simulative environment using a Digital Twin enables automated systems to be quickly and easily reconfigured by a virtual test in simulation. The recommissioning and reconfiguration of the real manufacturing system therefore requires less working time, is less error prone and thereby, reducing costs. However, such savings are only worthwhile if the effort of creating the digital twin is covered by the savings from the above-mentioned benefits. It is evident that the Digital Twin should be implemented by integrated software systems, such as product lifecycle management (PLM) systems, and should also be based on standards such as AML. The implementation of AML for PLM systems combines the benefits of both and enables the managed utilization of many different tools with standardized interface and thereby helps reducing the barriers for its creation.

Zusammenfassung

There has been much discussion about the future of automation in recent years. The topic of Industry 4.0 has been with us since around 2012 and has provided many impulses for the future, some of which have been implemented, are in development or research and will remain for some time to come. In this paper, we will give a glimpse at four key enablers of automation today and in the next 10 years. Fig. 1 depicts the future technologies which are based on the concept of Modularity which are going to enhance automation technology in the next decade. Apart from Modularity, the topics of Connectivity, Digital Twin and Autonomy are going to be key technologies in the coming developments and are going to determine the topic of automation over the next years.

Zusammenfassung

With the rise of the Internet of Things and Industry 4.0, the number of digital devices and their produced data increases tremendously. Due to the heterogeneity of devices, the generated data is mostly heterogeneous and unstructured. This challenges established approaches for knowledge discovery, which typically consume structured data from one source. The paper first describes aspects of data heterogeneity and their relevance for Industry 4.0 systems. Following, the upcoming challenges for different steps inside the knowledge discovery process for Industry 4.0 systems, such as for data integration and data mining, are discussed. Additionally, it mentions approaches to tackle them.

Zusammenfassung

Cyber-Physical Systems, characterized by networking capabilities and digital representations, offer many promising potentials for industrial automation. In an attempt to further enrich the system's digital representation by incorporating interdisciplinary models and considering a continuous and synchronized representation of it within the cyber layer, the concept of the Digital Twin emerged, enabling system monitoring, virtual commissioning, failure diagnosis and simulations by managing the Cyber-Physical Systems data along its lifecycle. To add further intelligence into the Digital Twin, the architecture of the intelligent Digital Twin was proposed. Nevertheless, managing and relating the complex and dynamic digital models as well as the heterogeneous data of the intelligent Digital Twin present open challenges. Due to their inherent extensibility and adaptability as well as their semantic expressiveness, Knowledge Graphs are a suitable concept to overcome these challenges and enable reasoning to gain new insights. Prominent applications of Knowledge Graphs are recommendation systems and exploratory search within the semantic web. However, there seems to be a lacking yet potential applicability for Knowledge Graphs in the industrial domain. Therefore, this contribution proposes a Knowledge Graph enhanced architecture of the intelligent Digital Twin, offering capabilities, which are internal linking and referencing, knowledge completion, error detection, collective reasoning and semantic querying. Based on the proposed concept, potential application fields for Knowledge Graph enhanced intelligent Digital Twin are addressed.

Zusammenfassung

Online error detection helps to reduce the risk of failure of safety-critical systems. However, due to the increasing complexity of modern Cyber-Physical Systems and the sophisticated interaction of their heterogeneous components, it becomes harder to apply traditional error detection methods. Nowadays, the popularity of Deep Learning-based error detection snowballs. DL-based methods achieved significant progress along with better results. This paper introduces the KrakenBox, a deep learning-based error detector for industrial Cyber-Physical Systems (CPS). It provides conceptual and technical details of the KrakenBox hardware, software, and a case study. The KrakenBox hardware is based on NVIDIA Jetson AGX Xavier, designed to empower the deep learning-based application and the extended alarm module. The KrakenBox software consists of several programs capable of collecting, processing, storing, and analyzing time-series data. The KrakenBox can be connected to the networked automation system either via Ethernet or wirelessly. The paper presents the KrakenBox architecture and results of experiments that allow the evaluation of the error detection performance for varying error magnitude. The results of these experiments demonstrate that the KrakenBox is able to improve the safety of a networked automation system.

Zusammenfassung

In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.

Zusammenfassung

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset.

Zusammenfassung

In recent years, the use of lithium-ion batteries has greatly expanded into products from many industrial sectors, e.g. cars, power tools or medical devices. An early prediction and robust understanding of battery faults could therefore greatly increase product quality in those fields. While current approaches for data-driven fault prediction provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in operational or environmental parameters. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real battery wear dataset. Online elastic weight consolidation delivers the best results, but, as with all examined approaches, its performance appears to be strongly dependent on task characteristics and task sequence.

Zusammenfassung

Autonomous driving is the key toward ecologic and economic mobility. Robo-Test at the University of Stuttgart has created a pilot environment that uses AI-based clustering to automatically derive test scenarios and thus validate automated driving systems efficiently and effectively.

Zusammenfassung

Autonomes Fahren ist der Schlüssel zu ökologischer und ökonomischer Mobilität. Robo-Test der Universität Stuttgart hat eine Pilotumgebung geschaffen, mit der mithilfe von KI-basierten Clustering automatisch Testszenarien abgeleitet und somit automatisierte Fahrsysteme effizient und effektiv validiert werden können.

Zusammenfassung

We are seeing a boom in the use of real-time position data to automate and optimize tasks in the field of production and logistics. Here we consider the reasons for this, show what has already proven to be industrially viable and give an overview of six current research efforts. We show which use cases have been automated by RTLS and where RTLS could play a role to further optimize production processes or material flows in the future.

Zusammenfassung

Die Flexibilisierung von Produktionsanlagen ist notwendig, um in einem volatilen Marktumfeld erfolgreich sein zu können. Durch die Bereitstellung und Pflege geeigneter Schnittstellen und Betriebsmodi, sowie dem Umbau von Anlagen, kann auf geänderte Anforderungen an die Produktion reagiert werden. Für die Realisierung derartiger hochflexibler und rekonfigurierbarer Anlagen ist ein erhöhter Engineering- und Testaufwand notwendig. Jede zusätzliche Funktion samt aller damit verbundener Abhängigkeiten müssen vor Inbetriebnahme mehrfach von Herstellern, Integratoren und Betreibern überprüft werden. Damit der resultierende erhöhte Testaufwand auch zukünftig handhabbar bleibt, werden in dieser Veröffentlichung folgende Ansätze vorgestellt, um ein effizientes und wirkungsvolles Testen im Bereich der flexiblen Produktion zu ermöglichen: Zum einen lässt sich durch eine auf Richtlinien basierende Modularisierung von Anlagen und Komponenten der Testaufwand reduzieren. Auch sorgen Agile Teams für eine verbesserte Kommunikation zwischen Entwicklungs- und Testabteilungen. Überdies ermöglicht die Anwendung von Digitalen Zwillingen einen effektiveren Informationsaustausch zwischen den Stakeholdern und Modellbasiertes Testen ermöglicht Vorteile bei der Testautomatisierung. Alle Ansätze sind jeweils eigenständig wirksam, aber zusammengenommen ergeben sich zusätzliche Synergieeffekte.

Zusammenfassung

Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks. Deep transfer learning offers mitigation by letting algorithms built upon previous knowledge from different tasks or locations. In this article, a modular deep learning algorithm for anomaly detection on time series datasets is presented that allows for an easy integration of such transfer learning capabilities. It is thoroughly tested on a dataset from a discrete manufacturing process in order to prove is fundamental adequacy towards deep industrial transfer learning – the transfer of knowledge in industrial applications’ special environment.

Zusammenfassung

Nowadays, formal methods are used in various areas for the verification of programs or for code generation from models in order to increase the quality of software and to reduce costs. However, there are still fields in which formal methods haven’t been widely adopted, despite the large set of possible benefits offered. This is the case for the area of programmable logic controllers (PLC). This article aims to evaluate the potential of formal methods in the context of PLC development. For this purpose, the general concepts of formal methods are first introduced and then transferred to the PLC area, resulting in an engineering-oriented description of the technology that is based on common concepts from PLC development. Based on this description, PLC professionals with varying degrees of experience were interviewed for their perspective on the topic and to identify possible use cases within the PLC domain. The survey results indicate the technology’s high potential in the PLC area, either as a tool to directly support the developer or as a key element within a model-based systems engineering toolchain. The evaluation of the survey results is performed with the aid of a demo application that communicates with the Totally Integrated Automation Portal from Siemens and generates programs via Fastsynth, a model-based open source code generator. Benchmarks based on an industry-related PLC project show satisfactory synthesis times and a successful integration into the workflow of a PLC developer.

Zusammenfassung

Digital Twins have been described as beneficial in many areas, such as virtual commissioning, fault prediction or reconfiguration planning. Equipping Digital Twins with artificial intelligence functionalities can greatly expand those beneficial applications or open up altogether new areas of application, among them cross-phase industrial transfer learning. In the context of machine learning, transfer learning represents a set of approaches that enhance learning new tasks based upon previously acquired knowledge. Here, knowledge is transferred from one lifecycle phase to another in order to reduce the amount of data or time needed to train a machine learning algorithm. Looking at common challenges in developing and deploying industrial machinery with deep learning functionalities, embracing this concept would offer several advantages: Using an intelligent Digital Twin, learning algorithms can be designed, configured and tested in the design phase before the physical system exists and real data can be collected. Once real data becomes available, the algorithms must merely be fine-tuned, significantly speeding up commissioning and reducing the probability of costly modifications. Furthermore, using the Digital Twin’s simulation capabilities virtually injecting rare faults in order to train an algorithm’s response or using reinforcement learning, e.g. to teach a robot, become practically feasible. This article presents several cross-phase industrial transfer learning use cases utilizing intelligent Digital Twins. A real cyber physical production system consisting of an automated welding machine and an automated guided vehicle equipped with a robot arm is used to illustrate the respective benefits.

Zusammenfassung

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components’ behavior depends on the process’ specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.

Zusammenfassung

Autonomous and automated systems are increasingly being used in IT such as finance, but also transport, medical surgery and industry automation. Yet, the distrust in their reliability is growing. This presentation introduces the validation of autonomous systems. We evaluate in practical situations such as automatic driving and autonomous robots different validation methods. The conclusion: Classic methods are relevant for coverage in defined situations but must be supplemented with cognitive test methods and scenario-based testing.

Zusammenfassung

Industrie 4.0 ist mittlerweile in den großen Unternehmen in Deutschland angekommen. Allerdings sind die deutschsprachigen Industriebetriebe von einer flächendeckenden Einführung noch weit entfernt. Gerade die mittelständischen Branchen der Fertigungsindustrie tun sich mit der Einführung und Umsetzung dieser komplexen Strategie schwer.

Zusammenfassung

Softwarearchitekturen sind mit Blick auf die Komplexität von Softwaresystemen ein zentraler Ansatzpunkt, um die Entwicklung, spätere Erweiterungen und die Wartung durchführen zu können. Daher wird in diesem Beitrag wird eine service-orientierte Architektur (SOA) vorgestellt, die dezentrale Logistikmodule in Form von dezentralen Intralogistiksystemen organisiert, sodass Werkstücke auf ihrem individuellen Weg durch das Produktionssystem gesteuert werden können. Dabei bieten einzelne Logistikmodule ihre Transportfähigkeiten in Form von sogenannten Services an. Der individuelle Transport wird durch die Koordinierung der Logistikmodule in einer service-orientierten Architekturermöglicht. Um die Koordinierung zu ermöglichen, wird hierfür ein schlankes und dadurch echtzeitfähiges, busunabhängiges, Kommunikations-Protokoll beschrieben.

Zusammenfassung

Die Häufigkeit von Änderungen der Produktionsanforderungen nimmt aufgrund wirtschaftlicher Volatilität, kürzerer Innovationszyklen und Produktlebenszyklen kontinuierlich zu. Daher ist eine Vorhersage aller möglichen Ziele eines Produktionssystems zur Entwurfszeit unmöglich und es ergibt sich erhöhter Rekonfigurationsbedarf zur Betriebszeit. Derzeit weist die Rekonfiguration von Produktionssystemen jedoch einige Schwachstellen auf, die in diesem Beitrag aufgezeigt werden. Außerdem wird die Zukunft der industriellen Automatisierung von Cyber-Physischen Produktionssystemen dominiert werden, welche vielversprechende Potentiale bieten. Folglich werden die Cyber-Physischen Produktionssysteme und einige ihrer Potentiale im Hinblick auf Rekonfiguration diskutiert. Um diese theoretischen Potentiale tatsächlich nutzen zu können, sind allerdings entsprechende Konzepte erforderlich, weshalb dieser Forschungsbeitrag ein grundlegendes Konzept für ein selbstorganisiertes Rekonfigurationsmanagement präsentiert.

Zusammenfassung

The capabilities of a cyber-physical production system are determined by their physical and virtual elements and their interaction. The interaction between virtual and physical entities must be precisely defined, and it is therefore necessary to develop capability models and requirements that enable self-reconfiguration of different elements and their relationships. Furthermore, an ultra-flexible production system requires communication and analysis of huge amounts of data as well as flexible, near-real-time communication between elements. This paper discusses and describes the functional and technical requirements of an ultra-flexible factory and presents an approach to the semantic structuring of elements and capabilities.

Zusammenfassung

Smart Manufacturing adopts an extensive amount of enabling technologies. Progress in ICT constantly drives innovations. Ubiquitous connectivity enabled by IoT technologies, increasing compute capacity and efficiency, and the subsequent push of digitalization in industry enabled new data-driven applications and the re-emergence of AI-technologies over the last decade. At the same time, the deficiencies of current infrastructures and system architectures are revealed when companies fail to adopt data-driven technologies extensively. This paper discusses how modern IT-architectures and infrastructure for cyber-physical production systems can enable their inherent self-x capabilities to pave the road towards higher levels of autonomy facilitated by data-driven technologies.

Zusammenfassung

Digital Twins will change how systems and products are engineered and operated. Individual virtual representations of assets help to develop, maintain and change single components or whole factories. Aerospace engineering, product design and intelligent manufacturing are hot spots for the use of Digital Twins. Simultaneously, globalized markets lead to a growing awareness of dependability and quality, which increases the importance of verification and validation. The Digital Twin could prove to be key enabler for efficient verification and validation processes. This paper presents the results of the literature review of approaches that use Digital Twins for verification and validation purposes. Many solutions have been found for a wide range of challenges in various fields of application. This survey discusses the underlying methods and the elements of Digital Twins already in use. Most research approaches focus on simulations and three methodological clusters of approaches sharing similar ideas were identified.

Zusammenfassung

The added value of a Digital Twin for reconfiguring manufacturing systems promises an increase in system availability, a reduction in set-up and conversion times and enables the manufacturing of customer-specific products. To evaluate this claim, this paper selects an architecture of the Digital Twin and realizes it on the basis of an application scenario for a cyber-physical manufacturing system. A case study is used to test the reconfiguration of a manufacturing system by comparing two different methods, one without and one with use of the Digital Twin. In this paper, the process steps of both reconfigurations are described and discussed in detail and a qualitative and quantitative evaluation of the reconfiguration results is presented. Finally, this paper gives an outlook on future research on intelligent automation of manufacturing systems using the Digital Twin.

Zusammenfassung

Networked and intelligent cyber-physical systems are being increasingly applied in automation technology. Surrounding parameters representing the ambient environment and the users interacting with these systems can greatly influence their operation and tend to be heterogeneous and dynamic. There is insufficient consideration for these parameters and how they relate to the system and can contribute to optimizing its analysis and operation. Enabling context-awareness for cyber-physical systems presents an approach to consider surrounding and situational factors. In this contribution, the design of context-aware cyber-physical systems is addressed with the goal to first introduce a fitting definition for context-aware cyber-physical systems as well as propose an approach to dynamically model the system's context in relation to its user and environment. The concept is shown for a user-centric pill dispenser, based on which the role as well as applicability of the context model is presented.

Zusammenfassung

The heterogeneity and dynamics of IoT systems pose new challenges for their simulation, especially for hardware-in-the-loop simulations, which can be met by modular co-simulation. Therefore, several existing co-simulation approaches are presented and their suitability for hardware-in-the-loop co-simulations is evaluated. A new concept for dynamic hardware-in-the-loop co-simulation of IoT systems with a multi-agent system is presented, where each IoT component is simulated in its own simulation tool. Each individual simulation is represented by an agent and is therefore able to dynamically enter a co-simulation at run-time, which enables a "plug-and-simulate" behavior. The presented concept is evaluated by a prototypical implementation.

Zusammenfassung

Die zunehmende Komplexität hochautomatisierter Systeme bringt neue Herausforderungen bei der Verwaltung ihrer Modelle entlang des gesamten Lebenszyklus des Systems mit sich - von der Kundenakquise über Engineering und Rekonfiguration bis hin zum Systemrecycling. Der Digitale Zwilling ist ein Konzept, welches über den gesamten Lebenszyklus eines Assets hinweg das Management dieser Modelle sicherstellen kann. Es unterstützt jedoch nicht die automatisierte Modellerweiterung. Hier setzt diese Arbeit an. Die Anreicherung des Digitalen Zwillings um Modellverständnis und KI-Algorithmen zur eigenständigen Modellerweiterung bildet die Grundlager des vorgestellten Konzepts. Über die intelligente Auswertung der Informationsmodelle -angereichert mit aktuellen Prozessdaten- erkennt der Digitale Zwilling, wenn Modelle an ihre Grenzen stoßen. Zwei mögliche Ursachen für diesen Sachverhalt werden genauer betrachtet: (1) es fehlt eine Fähigkeit oder Information (2) der Gültigkeitsbereich des Modells wurde verlassen. Für beide Zustände wird ein Verfahren vorgeschlagen, welches auf Basis kooperativer Information aus dem Wertschöpfungsnetzwerk automatisiert eine Lösung findet. Die Evaluierung des Konzepts anhand eines Szenarios aus der Logistik und aus der Produktion liefert vielversprechende Ergebnisse.

Zusammenfassung

Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.

Zusammenfassung

Increasing product variety and shortening product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing systems. A synchronized Digital Twin of the manufacturing system is one solution to face these challenges. In order to use the Digital Twin for reconfiguration, a major challenge is to keep the developed Digital Twin of a manufacturing system, which was created during the engineering process, synchronized with the real system after commissioning. To automatically synchronize the cross-domain models of a Digital Twin after the commissioning of a manufacturing system, the authors introduced the Anchor-Point-Method in their previous papers. In this paper, the realization of the Anchor-Point-Method based on an assistance system is presented and the functionality is evaluated. This assistance system enables having an up-to-date Digital Twin of a manufacturing system available during the entire life-cycle of a system. Finally, a qualitative and quantitative evaluation of the advantages of a synchronized Digital Twin for the reconfiguration of a manufacturing system is presented. For this purpose, an automated system has been digitally and physically designed and built. On this system, a reconfiguration using the synchronized Digital Twin was performed and compared with another reconfiguration without Digital Twin collected through a survey. The results show that the Digital Twin can reduce the time of the reconfiguration process by up to 58 percent.

Zusammenfassung

A requirement of future industrial automation systems is the application of intelligence in the context of their optimization, adaptation and reconfiguration. This paper begins with an introduction of the definition of (artificial) intelligence to derive a framework for artificial intelligence enhanced industrial automation systems: An artificial intelligence component is connected with the industrial automation system’s control unit and other entities through a series of standardized interfaces for data and information exchange. This framework is then put into context of the intelligent digital twin architecture, highlight the latter as a possible implementation of such systems. Concluding, a prototypical implementation on the basis of a modular cyber-physical production system is described. The intelligent Digital Twin realized this way provides the four fundamental sub-processes of intelligence, namely observation, analysis, reasoning and action. A detailed description of all technologies used is given.

Zusammenfassung

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear time-variant system dynamics. The present paper delivers a comprehensive overviewof existing LSTM cell derivatives and network architectures for time series prediction. A categorization in LSTM with optimized cell state representations and LSTM with interacting cell states is proposed. The investigated approaches are evaluated againstdefined requirements being relevant for an accurate time series prediction.These include short-term and long-term memory behavior, the ability for multimodal and multi-step ahead predictions and the according error propagation.Sequence-to-sequencenetworks with partially conditioning outperform the other approaches, such as bidirectional or associative networks, and are best suited to fulfill therequirements.

Zusammenfassung

Large-scale manufacturing systems consist of multiple complex process steps that are highly interdependent and characterized by dynamic nonlinear physics. These processes are subject to model uncertainties and external disturbances that cause reduced process stability and fluctuant product quality. To avoid defects and to ensure a high product quality, an adequate detection and control of disturbance effects is needed. Hence, the paper presents a novel adaptive control scheme with disturbance rejection property for discrete large-scale manufacturing systems. To model the complex process physics and disturbance effects, Long short-term memory (LSTM) networks are applied. These recurrent networks improve the modeling of time-variant systems regarding short-term and long-term dependencies. The adaptive control scheme is predicated on a self-learning and cooperative prediction of process and disturbance to achieve an improved performance. Based on this neural prediction, a compensation of disturbance effects and thus a part-centered quality optimization can be achieved. The approach is tested and evaluated for a plant of hot forging.

Zusammenfassung

Manufacturing processes are characterized by their temporal and spatial distributed nonlinear physics. Analytical models are not available and numerical models do not incorporate abnormal process effects that are not known to the engineer. These unknown anomalies cause reduced process stability and fluctuant product quality. To tackle the problem, numerous approaches for anomaly detection based on neural networks have been developed over the years. Long short-term memory (LSTM) networks have also been investigated intensively for prediction purposes. Current approaches lack in the capability of constructing prediction models for both process and anomaly behavior. Furthermore, they do not deliver a solution for short-term as well as long-term anomalies. Hence, the current paper presents a novel detection and prediction procedure based on a LSTM architecture to cooperatively predict process outputs and anomalies by using two separate but interacting models. The anomaly detector is realized as stacked LSTM auto-encoder and the cooperative prediction models are based on sequence-to-sequence networks with gated recurrent units for short-term and LSTM for long-term effects. The approach is evaluated within a real industrial environment by means of a production plant for hot forging at a German automotive supplier for metal components.

Zusammenfassung

It is expected that data-driven methods of artificial intelligence in the context of Industry 4.0 will shape the future of industrial manufacturing. Although the topic is very present in research, the extent of the actual use of these methods remains unclear. This article therefore analyzes scientific articles published between 2013 and 2018 to obtain statistical data on the use of artificial intelligence methods in industry. Special attention is paid to the types of training and evaluation data, the distribution in different industrial branches, the considered use cases and the geographical origin of these articles. The resulting findings are distilled into practical advice for decision-makers.

Zusammenfassung

Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.

Zusammenfassung

A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.

Zusammenfassung

The utilization of deep learning in the field of industrial automation is hindered by two factors: The amount and diversity of training data needed as well as the need to continuously retrain as the use case changes over time. Both problems can be addressed by deep transfer learning allowing for the performant, continuous training on small, dispersed datasets. As a specific example for transfer learning, a dual memory algorithm for computer vision problems is developed and evaluated. It shows the potential for state-of-the-art performance while being trained only on fractions of the complete ImageNet dataset at multiple locations at once.

Zusammenfassung

In this paper, a novel light-weight incremental class learning algorithm for live image recognition is presented. It features a dual memory architecture and is capable of learning formerly unknown classes as well as conducting its learning across multiple instances at multiple locations without storing any images. In addition to tests on the ImageNet dataset, a prototype based upon a Raspberry Pi and a webcam is used for further evaluation: The proposed algorithm successfully allows for the performant execution of image classification tasks while learning new classes at several sites simultaneously, thereby enabling its application to various industry use cases, e.g. predictive maintenance or self-optimization.

Zusammenfassung

Assessing the safety of complex Cyber-Physical Systems (CPS) is a challenge in any industry. Fault Injection (FI) is a proven technique for safety analysis and is recommended by the automotive safety standard ISO 26262. Traditional FI methods require a considerable amount of effort and cost as FI is applied late in the development cycle and is driven by manual effort or random algorithms. In this paper, we propose a Reinforcement Learning (RL) approach to explore the fault space and find critical faults. During the learning process, the RL agent injects and parameterizes faults in the system to cause catastrophic behavior. The fault space is explored based on a reward function that evaluates previous simulation results such that the RL technique tries to predict improved fault timing and values. In this paper, we apply our technique on an Adaptive Cruise Controller with sensor fusion and compare the proposed method with Monte Carlo-based fault injection. The proposed technique is more efficient in terms of fault coverage and time to find the first critical fault.

Zusammenfassung

Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification techniques for DL-based applications and propose adequate fault tolerance mechanisms. DL-based applications, like any other software, are susceptible to common random hardware faults such as bit flips, which occur in RAM and CPU registers. Such faults can lead to silent data corruption. Therefore, it is crucial to develop methods and tools that help to evaluate how DL components operate under the presence of such faults. In this paper, we introduce two new Fault Injection (FI) frameworks InjectTF and InjectTF2 for TensorFlow 1 and TensorFlow 2, respectively. Both frameworks are available on GitHub and allow the configurable injection of random faults into Neural Networks (NN). In order to demonstrate the feasibility of the frameworks, we also present the results of FI experiments conducted on four VGG-based Convolutional NNs using two image sets. The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy. These results help to identify the most critical operations and layers, compare the reliability characteristics of functionally similar NNs, and introduce selective fault tolerance mechanisms.

Zusammenfassung

The increasing complexity of modern automation systems leads to inevitable faults. At the same time, structural variability and untrivial interaction of the sophisticated components makes it harder and harder to apply traditional fault detection methods. Consequently, the popularity of Deep Learning (DL) fault detection methods grows. Model-based system design tools such as Simulink allow the development of executable system models. Besides the design flexibility, these models can provide the training data for DL-based error detectors. This paper describes the application of an LSTM-based error detector for a system of two industrial robotic manipulators. A detailed Simulink model provides the training data for an LSTM predictor. Error detection is achieved via intelligent processing of the residual between the original signal and the LSTM prediction using two methods. The first method is based on the non-parametric dynamic thresholding. The second method exploits the Gaussian distribution of the residual. The paper presents the results of extensive model-based fault injection experiments that allow the comparison of these methods and the evaluation of the error detection performance for varying error magnitude.

Zusammenfassung

Due to the increasing agility in the development process, shorter life cycles, and changing customer and legislator requirements, production systems must be adaptable. In addition to the ability to react flexibly to changing production requirements, the behavior of the production system must be reassured after a change. Formal verification methods are suitable for safeguarding safety-critical functions. However, this requires a high modeling effort, which inhibits the use of formal verification methods in practice. For reliable verification results, the quality and correctness of the models is decisive. Therefore, it is reasonable to analyze the underlying models. This paper presents two promising and mutually complementing approaches for the lifecycle-spanning safeguarding of production systems to overcome the challenges for the verification that a reconfiguration of the production systems brings with it during operation.

Zusammenfassung

Intelligente autonome Systeme sind branchenübergreifend in Erprobung und Nutzung. Gleichzeitig wächst das Misstrauen in deren Zuverlässigkeit. Die aktuell genutzte Validierung mit Funktionstests und dem Ausprobieren mit Brute-Force-Tests sind zwar aufwendig, aber bleiben immer unvollständig und intransparent. Um Zuverlässigkeit und Vertrauen in der Gesellschaft zu erreichen, benötigen wir intelligente Validierungstechniken, die auch dynamische Änderungen und Lernprozesse abdecken. Dieser Artikel bietet Einblicke in die Validierung autonomer Systeme. Ein erstes Fazit: Klassische Verfahren müssen durch kognitive Testmethoden mit systematischer Szenarienabdeckung ergänzt werden.

Zusammenfassung

Full validation of the safety of an autonomous vehicle requires excessive amounts of testing and driving hours, which is impossible to achieve without virtual simulations. Therefore, companies seek efficient and smart testing strategies to virtually validate the safety of autonomous vehicle functions aiming to achieve lower costs and less time to market. Simulation-based testing for autonomous and connected vehicles is essential for the future generation of transportation systems.

Zusammenfassung

In response to an aging society, the development and application of medical assistance systems is increasing and becoming an integral part of various daily life activities. To enable effective assistance, designing intuitive and elderly-appropriate user interfaces is necessary and represents a challenge due to the lack of standardized design processes. A possible approach for the design is to apply the usability engineering process based on the IEC 62366 standard for medical devices. In this contribution, the design process of a user interface for an automated pill dispenser is shown based on applying the standard, a literature review and a user evaluation.

Zusammenfassung

As the population is aging, the need for assistance systems in the ambient assisted living domain is high. With the increased availability of data provided by ambient sensors, devices, wearables and the improvements in the artificial intelligence field, there lies great potential for inferring knowledge about the elderly and the context, in which assistance is provided by a system. Being able to correlate and learn from dynamic and variable data enables a continuously optimized, individualized and effective assistance. The goal of this research is to develop an underlying, reusable concept for intelligent and user-centered assistance. Therefore, we experiment with several automation systems as use cases for elderly assistance.

Zusammenfassung

The demographic change poses technical, social and eco-nomic challenges, as more people require assistance for maintaining a healthy and good quality life. The emergence of technical assistance systems applying communication and information technologies as well as artificial intelli-gence present viable solutions for these challenges. The goal of this research work is to develop a concept for intel-ligent and user-centered assistance systems for the elderly at the example of a medication assistance system, a medi-cine cabinet as well as an automated walker. A significant aspect when designing the proposed assistance systems is their usability by the elderly. Although aging brings forth a gradual deterioration on a sensory, cognitive and muscular level, this declining process is variable for each individual as well as for one individual over time. An assistance sys-tem should, however, be made utilizable by everyone. One measure to develop an elderly-appropriate assistance sys-tem is to include a multimodal user interface. An interface is defined to be multimodal if it offers more than one inter-action method. This includes auditory, visual, and haptic interactions 1,2. Subsequently, the user can interact with the system regardless of the symptoms of aging which man-ifest over time 3. Another aspect is the ability for the el-derly to use the system and benefit from the emerging tech-nologies, without directly having to deal with its increas-ingly complex structure. Hiding the system’s complexity from the user is reasonable as many elderly people are not familiar with modern technologies. Arguing that both as-pects play a major role when designing an elderly-appro-priate and utilizable assistance system, applying voice con-trol can provide a natural access to an assistance system. It enables multimodality as well as ease of use as verbal com-munication is the default and natural communication method among humans. As an approach to make assistance systems elderly-appro-priate and -useable, in this contribution, we focus on de-signing voice control for the medication assistance system, namely, a pill dispenser.

Zusammenfassung

Der Systementwicklungsprozess nimmt immer mehr an Komplexität zu, da die Systeme selbst immer komplexer werden. Gleichzeitig Vermischen sich die verschiedenen Disziplinen wie Maschinenbau, Elektrotechnik und Softwaretechnik zunehmend, so dass Unternehmen einer Disziplin sprunghafte Komplexitätszuwächse bei ihren Systemen und in ihrer Entwicklung haben. Deshalb wird in dieser Veröffentlichung ein Konzept eines Sprachassistenten erarbeitet, der durch eine Entwicklungsphase führt. Daraus geht hervor, dass die Software zur Unterstützung der Entwicklung ein Informationsmodell benötigt, um die Daten des entwickelten Systems zu speichern und diese mit dem vorhandenen Wissen zu verbinden. Dieses Wissen kann entweder intern oder im Web vorhanden sein. Der Entwicklungsprozess soll daher Kooperation unterstützen, so dass die Assistenzsoftware und Ingenieure miteinander interagieren.

Zusammenfassung

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.

Zusammenfassung

For the verification of systems using model-checking techniques, symbolic representations based on binary decision diagrams (BDDs) often help to tackle the well-known state-space explosion problem. Symbolic BDD-based representations have been also shown to be successful for the analysis of families of systems that arise, e.g., through configurable parameters or following the feature-oriented modeling approach. The state space of such system families face an additional exponential blowup in the number of parameters or features. It is well known that the order of variables in ordered BDDs is crucial for the size of the model representation. Especially for automatically generated models from real-world systems, family models might even be not constructible due to bad variable orders. In this paper we describe a technique, called iterative variable reordering, that can enable the construction of large-scale family models. We exemplify feasibility of our approach by means of an aircraft velocity control system with redundancy mechanisms modeled in the input language of the probabilistic model checker PRISM. We show that standard reordering and dynamic reordering techniques fail to construct the family model due to memory and time constraints, respectively, while the new iterative approach succeeds to generate a symbolic family model.

Zusammenfassung

Unmanned aerial vehicles are safety-critical systems. A failure in the system behavior may lead to hazards and serious consequences. The assessment of the system dependability characteristics that include safety and reliability early in the design phase is a beneficial task that helps to avoid expensive redesign and iterations in later design phases. Unmanned aerial vehicles typically comprise several independent components that communicate and share data via a network. Adequate timing is crucial for proper system behavior. However, the timing is affected by various system faults, e. g., network induced delays, which typically have a stochastic nature. The focus of this paper is on the effective model-based application of the previously published method for the analysis of timing errors to a realistic case study model of a quadcopter. The holistic solution proposed in this paper integrates the SysML modeling and annotating approach, the automated transformation of the SysML model to a Stochastic Petri Net, and the identification, formalization, and analysis of timing properties. The case study model is annotated with the timing parameters recorded from a real-world system. The results of the conducted sensitivity analysis reveal the effects of parameter changes in the annotated timing properties. These quantitative results may be used as input for the error propagation analysis, allow the assessment of the system dependability and enable to compare different design options concerning the occurrence of timing errors. Keywords: UAV, Dependability, SysML Activity, Petri net, Timing analysis, Safety-Critical system.

Zusammenfassung

Model-based fault injection methods are widely used for the evaluation of fault tolerance in safety-critical control systems. In this paper, we introduce a new model-based fault injection method implemented as a highlycustomizable Simulink block called FIBlock. It supports the injection of typical faults of essential heterogeneous components of Cyber-Physical Systems, such as sensors, computing hardware, and network. The FIBlock GUI allows the user to select a fault type and configure multiple parameters to tune error magnitude, fault activation time, and fault exposure duration. Additional trigger inputs and outputs of the block enable the modeling of conditional faults. Furthermore, two or more FIBlocks connected with these trigger signals can model chained errors. The proposed fault injection method is demonstrated with a lower-limb EXO-LEGS exoskeleton, an assistive device for the elderly in everyday life. The EXO-LEGS model-based dynamic control is realized in the Simulink environment and allows easy integration of the aforementioned FIBlocks. Exoskeletons, in general, being a complex CPS with multiple sensors and actuators, are prone to hardware and software faults. In the case study, three types of faults were investigated: 1) sensor freeze, 2) stuck-at-0, 3) bit-flip. The fault injection experiments helped to determine faults that have the most significant effects on the overall system reliability and identify the fine line for the critical fault duration after that the controller could no longer mitigate faults.

Zusammenfassung

Modern safety-critical systems are heterogeneous, complex, and highly dynamic. They require reliability evaluation methods that go beyond the classical static methods such as fault trees, event trees, or reliability block diagrams. Promising dynamic reliability analysis methods employ probabilistic model checking on various probabilistic statebased models. However, such methods have to tackle the well-known state-space explosion problem. To compete with this problem, reduction methods such as symmetry reduction and partial-order reduction have been successfully applied to probabilistic models by means of discrete Markov chains or Markov decision processes. Such models are usually specified using probabilistic programs provided in guarded command language. In this paper, we propose two automated reduction methods for probabilistic programs that operate on a purely syntactic level: reset value optimization and register allocation optimization. The presented techniques rely on concepts well known from compiler construction such as live range analysis and register allocation through interference graph coloring. Applied on a redundancy system model for an aircraft velocity control loop modeled in SIMULINK, we show effectiveness of our implementation of the reduction methods. We demonstrate that model-size reductions in three orders of magnitude are possible and show that we can achieve significant speedups for a reliability analysis.

Zusammenfassung

A failure in the behavior of safety-critical systems such as Unmanned Aerial Vehicles (UAV) may lead to hazards and serious consequences. Therefore UAVs must perform correctly throughout their intended life cycle. However, the functional system specification is not sufficient to ensure proper performance.The assessment of non-functional reliability, safety, or timing requirements in the early development phases helps to prevent conceptually wrong decisions. UAVs usually contain complex and tightly coupled hardware and software components that influence each other. Hence, a holistic approach to analyze the non-functional properties is preferable. The concept of phased mission systems enables the specification of individual requirements for different phases. This allows more accurate predictions of the success or failure of a mission.This paper presents a holistic model-based dependability assessment approach that supports system-software co-engineering for UAVs. The approach combines stochastic timing analysis and the data error propagation analysis and enables the consideration of phase-specific requirements as well as the identification, formalization, and analysis of timing properties. The solution proposed in this paper is centered around the Systems Modeling Language (SysML) and the systematic transformation of annotated SysML models to Probabilistic Model Checking (PMC) accessible Markov chain models

Zusammenfassung

In diesem technischen Bericht wird das agentenbasierte Assistenzsystem aus Nutzersicht beschrieben, das im Rahmen des DFG-Projekts FlexA entwickelt wurde. Das Assistenzsystem unterstützt den Benutzer bei der Anwendung der in FlexA entwickelten Methodik MHFW18. Konzipiert wurde das Agentensystem mithilfe der Gaia-Methode, die Umsetzung erfolgte mithilfe von JADE (Java Agent DEvelopment Framework). JADE stellt dem Entwickler vorgefertigte Klassen zur Entwicklung von Software-Agenten zur Verfügung und bietet darüber hinaus FIPA-konforme Kommunikationsmöglichkeiten zwischen den Agenten. Nähere Informationen zur Konzeption des Agentensystems sind in MWHF17 beschrieben. Das Assistenzsystem ist zweigeteilt aufgebaut und besteht aus (1) einem Tool zur Verwaltung des Modells des Automaten (PPR Model Manager) sowie (2) der eigentlichen Methodik von FlexA, um Produktionsanfragen zu überprüfen und Flexibilisierungsvorschläge zu generieren. Der PPR Model Manager wurde implementiert, da mit steigender Größe der betrachteten Automaten eine manuelle Modellerstellung zu aufwändig und fehleranfällig wurde. Zudem deckte kein am Markt befindliches Modellierungswerkzeug von Haus aus alle benötigten Funktionen ab, weshalb eine Eigenentwicklung vorgenommen wurde. Eine künftige Portierung des Assistenzsystems auf verbreitete Austauschformate wie AutomationML ist denkbar.

Zusammenfassung

In the development of automated systems in the field of industrial automation, engineers often have to work on many tasks under time pressure, including simulations. In order to support engineers in these complex simulation tasks, a framework is presented which coordinates and calculates simulations based on structured knowledge in a parallelized, decentralized way. Different approaches for a problem are simulated automatically and compared with each other. Therefore, complex problems, meaning large-scale ones that often consist of coupled problem parts, are decomposed into partial problems. Their approaches are subsequently processed separately each using the most suitable software tool. Cooperating software agents are utilized to achieve a flexible coordination of decentralized simulations. These autonomous software units communicate with each other to control the parallelized activities during a simulation project. The presented concept is evaluated based on the application fields of control design and multiphysics simulation. Hence, multi-agent systems (MAS) were generated prototypically using the developed framework. As an addition to the framework for parallel simulation, a concept for supplementing the MAS with a case-based reasoning component is presented. Using the case-based reasoning addition, the performance of the simulations was further improved. Due to the varying computation time of the different approaches, analyzed in parallel, results are often available earlier. The MAS is also capable of improving the quality of the results because of the automatic analysis and comparison of different approaches.

Zusammenfassung

In the automation domain, OPC UA can be considered one of the most important communication protocols for IIoT applications. According to a study of McKinsey the potential economic impact of the IoT within factories may reach up to 3,7 Trillion Dollar by 2025. To unlock this potential it is very important to bridge the interoperability gap. Since V1.04 OPC UA also supports session-less clients and therefore allows the development of RESTful clients which can be used to address the cross-domain interoperability issue. However, currently, RESTful clients cannot use Subscriptions, because the corresponding Service Sets are not supported for session-less clients. In this paper, we will present how Subscriptions can be introduced for session-less clients. Furthermore, we outline how Group-Subscriptions can be introduced for session-less as well as session-based clients. Finally, our evaluation shows that Group-Subscriptions significantly outperform standard Subscriptions if the Subscription can be shared by several clients.

Zusammenfassung

OPC UA is one of the most important communication protocols for IIoT applications in the automation domain. One important requirement for Industry 4.0 scenarios is standardized semantics, which is introduced into OPC UA through Companion Specifications. Nevertheless, OPC UA still lacks a comfortable interface for accessing these huge graphs, especially on the edge- and cloud-layer. Of course, there is a query language specified for OPC UA. However, to the best of our knowledge, there is no publicly available implementation. In this paper, we will present how SPARQL can be used to query OPC UA graphs natively and also will present rules to transform OPC UA queries into SPARQL queries. Furthermore, we will highlight some issues of OPC UA Query and how they can be bypassed by native SPARQL. Finally, we were able to correctly execute all nine example queries of OPC UA Part 4 Annex B (complex examples) with both approaches.

Zusammenfassung

This paper highlights the necessity for a digital twin of production systems in the automotive industry. The survey on the use case of a digital twin of the production line is conducted with the production planning department of a major automobile manufacturer. The central part of the paper describes the current and future challenges for production planners in integration planning, which are determined by a survey. There are new trends in the automotive industry identified as a result of this survey. The study identifies exactly the information required by a digital twin of the production plant to improve the integration planning process. Furthermore, this survey provides answers to the benefits of integration planning by an automatically generated digital twin.

Zusammenfassung

The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cases are conceptualized. There lacks, however, a clear, encompassing architecture covering necessary components of a Digital Twin to realize various use cases in an intelligent automation system. In this contribution, the added value of a Digital Twin in an intelligent automation system is highlighted and various existing definitions and architectures of the Digital Twin are discussed. Flowingly, an architecture for a Digital Twin and an architecture for an Intelligent Digital Twin and their required components are proposed, with which use cases such as plug and produce, self-x and predictive maintenance are enabled. In the opinion of the authors, a Digital Twin requires three main characteristics: synchronization with the real asset, active data acquisition from the real environment and the ability of simulation. In addition to all the characteristics of a Digital Twin, an Intelligent Digital Twin must also include the characteristics of Artificial Intelligence. The Intelligent Digital Twin can be used for the realization of the autonomous Cyber-Physical Production Systems. In order to realize the proposed architecture for a Digital Twin, several methods, namely the Anchor-Point-Method, a method for heterogeneous data acquisition and data integration as well as an agent-based method for the development of a co-simulation between Digital Twins were implemented and evaluated.

Zusammenfassung

Die Konzepte des digitalen Zwillings werden sowohl in der Wissenschaft als auch in der Industrie immer relevanter. Daraus ergeben sich viele verschiedene Sichtweisen. Ein Überblick über das vorherrschende Verständnis wird in diesem Beitrag gegeben. Darüber hinaus werden die notwendigen Aspekte eines digitalen Zwillings erläutert und der Bedarf an zukünftiger Forschung zu Schlüsseltechnologien (Technologiekomponenten) im Kontext digitaler Zwilling begründet. Auf dieser Grundlage werden mögliche Vorteile eines digitalen Zwillings über seinen Lebenszyklus dargestellt. Weiterhin wird in diesem Beitrag ein ausgearbeiteter Vorschlag für die Architektur eines digitalen Zwillings präsentiert. Der Beitrag schließt mit einem Prototyp, in dem Artefakte und Konzepte des digitalen Zwillings eines Lkws exemplarisch implementiert sind.

Zusammenfassung

This contribution describes a novel approach for developing a medication assistance system, namely a pill dispenser. The approach supports mobile and stationary use and applies IoT features for the system to be interconnected and remain synchronized. Based on a user survey and a literature review, functional requirements for the pill dispenser have been derived and use case scenarios conceptualized, followed by a rudimentary software and hardware development.

Zusammenfassung

The digital twin is a great term that is frequently used in the context of digitalization and Industry 4.0. In discussions with other researchers and colleagues about the digital twin, it oftentimes becomes clear that everyone understands something different about the digital twin. This paper therefore compares the different facets of digital twins in the industrial environment of production. Different applications are shown, and a list of added values of a digital twin is derived based on these applications. The paper also covers the different facets of digital twins in the automotive industry and evaluates their practical benefits. In addition, the different technologies for realizing a digital twin are examined, and a concept for modeling the lifecycle of a production line via the digital twin is presented.

Zusammenfassung

The communication protocol OPC UA is one of the most important IIoT enablers within the automation domain. OPC UA not only aims to provide interoperability on the transport layer, but also interoperability of the semantic layer shall be addressed based on so-called Companion Specifications. However, the lack of OPC UA formal semantics makes automatic validation of OPC UA data models impossible. Another drawback is the shortage of available tools for OPC UA, such as an implementation of the query engine for the specified OPC UA query language. In this paper we provide a formal translation of OPC UA models to the Semantic Web standard OWL, thus making OPC UA implicit semantics, that is described in the documentation, explicit, by means of OWL axioms. Moreover, we outline how this mapping can be used to offer validation and querying of OPC UA data models based on already existing Semantic Web technology.

Zusammenfassung

Prozessanomalien in Form von unbekannten nichtlinearen Effekten sind ursächlich für Qualitätsschwankungen und Ausschussteile in der diskreten Fertigung. Das Prozessverhalten ist hochgradig nichtlinear und kann u. a. aufgrund seiner zeitlichen und örtlichen Verteilung nicht in einem analytischen Modell gefasst werden. Aus diesem Grund wird ein datengetriebenes Verfahren entwickelt, um Anomalien in Form von Modellunsicherheiten und unbekannten Nichtlinearitäten zu kompensieren. Die Kompensation wird durch ein Neuronales Netz realisiert. Auf die entwickelte Architektur des Neuronalen Netzes wird im Detail eingegangen. Es generiert ein Signal zur Anpassung der Stellgrößen, sodass diese so geändert werden, dass der Einfluss der Anomalien auf das Qualitätsergebnis des Prozesses ausgeglichen wird und die Qualität länger in der Toleranz gehalten werden kann. In Kapitel 2 wird der Stand der Technik und Forschung beschrieben. Dabei werden vor allem bereits bestehende Ansätze hinsichtlich des Einsatzes Neuronaler Netze reflektiert. In Kapitel 3 wird eine formale Beschreibung der Prozesskette als Ganzes sowie der Prozesse im Einzelnen vorgenommen. Dazu wird die nichtlineare Systembeschreibung um Interdependenzen entlang der Prozesskette erweitert. Kapitel 4 widmet sich dem Konzept zur Prädiktion und Kompensation dynamischer Anomalien. Dabei wird zunächst der grundlegende Ansatz erläutert, bevor die dafür entwickelte Netzarchitektur vorgestellt wird. Diese wird dann zur Erzeugung der Kompensation in den geschlossenen Regelkreis zur Qualitätssicherung integriert. In Kapitel 5 wird auf die Prozessketten zur Evaluierung eingegangen, bevor dann die empirischen Ergebnisse präsentiert werden. In Kapitel 6 erfolgen eine Zusammenfassung und ein Ausblick.

Zusammenfassung

Machine learning algorithms rely on a broad database for high quality results. However, studies show that many companies are not willing to share their data with other companies, for exam-ple in the form of a shared data cloud. Therefore, the goal should be to make efficient machine learning possible with decentralized data storage that allows confidential data to remain in the respective company of origin. This article presents a new concept in this respect and analyses its potential for intelligent automation systems taking predictive maintenance as an example. The feasibility of the concept using various existing approaches will be discussed, before po-tential benefits for plant operators and manufacturers, with particular consideration of the per-spective of small and medium-sized companies, will be discussed.

Zusammenfassung

The demand of customer-specific products leads to a fundamental change to manufacturing facilities. To adapt the facilities to new product types, frequently occurring functionality changes in industrial automation systems are expected. Functionality changes are primarily implemented by software changes. These software changes within the operation phase can be implemented, for instance, by over-the-air software updates or ad hoc integration of new components. The effects of these changes are often difficult to estimate, especially in distributed automation systems. This mainly poses a challenge on production line operators, who are required to validate their automation systems after functionality changes have been executed. The goal of this contribution is to assist production line operators in the validation process of their automation systems after software changes. Formal verification methods can support the operators, due to its fully automated execution. However, the creation process of the behavior models needed for the formal verification is complex and error-prone. This is why formal verification is usually not used. Hence, a model-based technique is presented to automate this creation process. By means of this, the subsystem affected by the software change is automatically identified and subsequently a suitable input to a model-based verification tool is generated. The concept is based on the generation of a system model by composing the Petri net models of components within the automation system. In order to identify affected components, an impact analysis is performed, starting from the component in which a modification occurred. Followingly, a tailored subsystem is composed using the component models necessary for verification. This subsystem is applied to verify the system requirements for the affected components. To evaluate the applicability of the concept in the field of industrial automation, a distributed automation system was implemented. A service-oriented, OPC-UA-based, control network is thereby used to implement a technical process. Furthermore, a configuration interface enables change of the components at runtime. This emulates over-the-air updates and ad hoc networking. The concept is implemented with the demonstrator “TestIAS.” This test device detects software changes within the automation system and verifies them automatically according to the model-based approach presented. An empirical evaluation was performed with ten different reconfiguration scenarios showing functional changes. In addition, based on the time measurements of the time saving due to the impact analysis, the efficiency enhancement is substantiated.

Zusammenfassung

Autonomous systems are widely used. Yet, for lack of transparency, we are increasingly suspicious of their decision making. Traditional validation, such as functional testing and brute force, won’t help, due to complexity and cost. To achieve dependability and trust we need dedicated, intelligent validating techniques that cover, for instance, dynamic changes and learning. Michael Weyrich and I provide industry insights into validating autonomous systems. I look forward to hearing from both readers and prospective authors about this article and the technologies you want to know more about.

Zusammenfassung

Automated and connected driving up to autonomous vehicles is increasingly being used. But the distrust in their reliability is growing. The underlying algorithms are difficult to understand and thus intransparent. Traditional validations are complex, expensive and expensive. In addition, no transparent coverage for regression strategies for upgrades and updates is achieved. In this article Vector Consulting and the IAS of the University of Stuttgart show that classical methods have to be supplemented with cognitive test methods.

Zusammenfassung

Automatisierte und autonome Fahrsysteme kommen zunehmend zum Einsatz. Doch das Misstrauen in deren Zuverlässigkeit wächst. Die zugrundeliegenden Algorithmen sind schwer nachvollziehbar und damit intransparent. Herkömmliche Validierungen sind komplex, aufwendig und teuer. Zudem wird keine transparente Abdeckung bei Regressionsstrategien für Upgrades und Updates erreicht. Vector Consulting und das IAS der Universität Stuttgart zeigen in diesem Beitrag, dass klassische Validierungsverfahren durch kognitive Testmethoden ergänzt werden müssen.

Zusammenfassung

Der Mobilfunkstandard 5G bietet vielschichtige Möglichkeiten und Chancen für eine neue Form der drahtlosen Kommunikation in allen Bereichen: vom Consumer-Bereich über die Medizintechnik bis hin zu vielen verschiedenen Applikationen in Automatisierungstechnik und Industrie. Eine ganze Branche ist ins Träumen geraten - diesen Eindruck konnte man bei einem Besuch der diesjährigen Hannover Messe gewinnen. Dort wurden unterschiedlichste Möglichkeiten der mobilen Kommunikation auf Basis von 5G aufgezeigt und so gut wie alle namhaften Hersteller präsentierten eine 5G-Anwendung. Auch die großen Telekommunikationsunternehmen werden schon bald neue Angebote für 5G in den Frequenzbereichen 2 GHz und 3,4 bis 3,7 GHz entwickeln. Zudem ist damit zu rechnen, dass sie nicht nur Kommunikationsdienste anbieten, sondern auch die Gelegenheit nutzen werden, um damit verbundene Angebote wie "Dinge-Gateways", spezielle "DeviceClouds" oder ganze Plattformen zum Informations- und Datenmanagement anzubieten.

Zusammenfassung

Die Anforderungen an die Logistik, speziell an die Intralogistik, sind in den letzten Jahren aufgrund steigender Heterogenität und Dynamik innerhalb der Produktion deutlich gestiegen. Diese Entwicklung wird es nötig machen, dass Intralogistiksysteme regelmäßig an sich ändernde Anforderungen angepasst werden, damit Unternehmen konkurrenzfähig bleiben können. Bevor das Intralogistiksystem auf die neuen Gegebenheiten angepasst werden kann, muss eine Überprüfung von Auf-wand und Nutzen stattfinden. Der Zeitpunkt einer Rekonfiguration ist stark abhängig von Umfeldbedingungen. Um Intralogistiksysteme unter sich ändernden Anforderungen zu analysieren und anzupassen, eignen sich Simulationen. Hierzu werden in diesem Forschungsbericht unterschiedliche Möglichkeiten der Simulation sowie mögliche Umfeldbedingungen von Intralogistiksystemen dargestellt. Darüber hinaus werden die Potenziale der Simulation für die Rekonfiguration sowie für das Abbilden der Umfeldbedingungen erörtert.

Zusammenfassung

In a mobile industrial fleet application different machinery is powered by each its own reciprocating internal combustion engine. EKU Power Drives (EKU) is focusing on increasing the energy efficiency of these fleet applications. Therefore, the combustion engines are enhanced to mild hybrid units and connected to a smart grid. This semi-automated fleet setup allows to dynamically share load between units or manage the usage of equipment to streamline operational procedures. Single combustion engines are entirely replaced by electric drives and powered by the excess power of the remaining hybrid engines and the grid. The increased load on the remaining engines results in specific fuel consumption advantages. This increases the efficiency of the entire fleet. Economically the achieved fuel savings are only a portion of the gained benefits. On the fleet perspective the necessary number of engines has been reduced and the individual load on the remaining has increased. Operators´ biggest cost savings result from less different engines to maintain and store spare parts. With the power need of the grid and the capability of the electrified auxiliary engine drives, concepts for electric motors and batteries are defined. A suitable net topology concept is developed, and the independent grid is controlled by a distributed cooperative closed loop control. This enables a configuration-less connection setup between different machinery of the fleet.

Zusammenfassung

The demographic change facing Germany and Europe leads to a growing importance of user-centered products for the elderly. Especially the increase of patient multimorbidity leads to a growing necessity of future healthcare solutions. In terms of medication management, a lack of reliable pill intake support for the elderly can be observed. This research project develops an approach for a fully automated pill management system. Special focus in this contribution lies on the user-centered design process for the elderly.

Zusammenfassung

The Institute of Industrial Automation and Software Engineering belongs to the Department of Electrical Engineering and Information Technology of the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. The research and teaching of the institute focuses on the topic of software systems for automation technology. We see ourselves as a connection of the product and plant automation in the research disciplines of information technology, software technology and automation.

Zusammenfassung

Das Institut für Automatisierungstechnik und Softwaresysteme gehört dem Fachbereich Elektrotechnik und Informationstechnik der Fakultät Informatik, Elektrotechnik und Informationstechnik der Universität Stuttgart an. Die Forschung und Lehre des Instituts konzentriert sich auf das Thema Softwaresysteme für die Automatisierungstechnik. Dabei verstehen wir uns als Brückenkopf der Produkt- und Anlagenautomatisierung Forschungsdisziplinen der Informationstechnik, Softwaretechnologie und Automatisierung.

Zusammenfassung

Manufacturing individual products requires individual production. To implement the production of unique products with traditional engineering approaches is thoroughly complex. Therefore, decentralized production systems on the basis of cyber physical systems (CPS) promise a realization schema and thereby, the reduction of the overall systems’ complexity. Cyber physical systems are rather more complex in communication, time requirements and coordination, but are less complex from a modularization point of view and enable new ways of diagnosis and maintenance. The digitalization of production and new concepts of Industry 4.0 enable direct machine to machine communication, exchange of information and making of common decisions. An approach for future manufacturing in which individual products are scheduled incrementally is presented in this paper. In the presented approach, the product invites itself production resources to offer process steps in an auction. The product automatically ranks the offers according to requirements which are predefined by the customer. The best bids are asked to optimize their offers within a negotiation process. To evaluate our approach, a software-agent based demonstrator was implemented.

Zusammenfassung

The paradigm of multi-agent systems is used in various application scenarios like manufacturing control or multi-agent assistance systems that aim at providing assistance to the user of the system in a specific task or situation. With an increasing number of applications of multi agent assistance systems in the industrial automation domain, the different architectures of these applications can be compared and differences and similarities can be identified. This paper presents the results of the literature review of 18 agent-based assistance systems from different domains. Within the reviewed architectures, seven different agent types could be identified. In a further step, design patterns based on the application domain and the level of automation have been identified and discussed. These design patterns can be used to facilitate the development of future multi agent assistance systems.

Zusammenfassung

The concept of cyber-physical systems introduces new technologies that enable new kinds of functionalities. At the same time, these technologies also pose new requirements for the reliability calculation of such systems. Runtime data of the system can be made available worldwide through the networking of components. This offers the possibility to use that data for a more accurate dynamic reliability calculation. Another aspect to consider in reliability calculation of CPS is that the system structure is not yet known at the time of system development and is likely to change frequently over time. This paper presents a multi-agent-based assistance concept for dynamic reliability calculation of cyber-physical systems. The failure rate which is usually assumed to be constant, is divided into time-dependent partial failure rates which are calculated dynamically by the agents. To perform this calculation, the agents gather relevant information from various sources and collaborate with each other. The paper also shows the influence of dynamic factors on the reliability of a system using a software prototype.

Zusammenfassung

Abstract Challenges on safeguarding distributed automation systems arise due to their increasing complexity and changeability. Functional changes in automation systems are mainly conducted by software modifications. Especially in distributed automation systems, the impacts of software modifications are difficult to estimate. Mainly, this will challenge plant operators who have to safeguard their automation systems after functionality changes were executed. If behaviour models of the automation systems are available, model-based techniques are suitable to estimate the impacts of software modifications on other system components. In fact, behaviour models of distributed automation systems are seldom available or maintained, due to the high complexity of the overall system and the changing structure caused by reconfigurations or software modifications. This often prevents the application of model-based techniques. This contribution presents a model-based approach with which the impacts of software modifications can be recognized and affected subsystems can be safeguarded efficiently by model-based verification methods. To achieve this an impact analysis is performed, identifying requirements which are affected by software modifications. As the behaviour models that are necessary to verify the identified requirements are seldom available, the necessary models are generated automatically. The approach is evaluated with modification to a large scale automation system.

Zusammenfassung

The heterogeneity and dynamic of IoT-systems pose new challenges for their simulation, which can be met by a modular co-simulation. Therefore several existing co-simulation approaches are presented and evaluated. A new concept for a dynamic co-simulation of IoT-systems utilizing a multi-agent-system is presented, wherein each IoT-component is simulated in a separate simulation tool. Each separate simulation is represented by an agent, and therefore able to enter a running co-simulation dynamically during runtime, which allows for a “Plug-andSimulate” behavior. The connection between agents and simulation tools is realized by an interface concept. The presented concept is evaluated by a prototypical implementation.

Zusammenfassung

This paper presents an intelligent approach to support engineers with performing computational simulation of new developments and prototypes. With multiple interacting physical effects and large three dimensional models the choice of the right solution strategy is crucial for a correct solution and an acceptable calculation time. The presented multi-agent system can solve these simulation tasks using distributed heterogeneous computation resources with the objective to reduce the calculation time. An important factor for the criterion time is the choice of the linear solver. Here a case-based reasoning concept is introduced to improve the decisions in the multi-agent system. Allowing each agent to solve its problem part by using appropriate solution methods, a decentralized architecture with autonomous software agents is provided.

Zusammenfassung

OPC UA is one of the most important communication protocols for Industry 4.0 applications in the automation domain. However, to really reach the status of an Internet-of-Things protocol, OPC UA also has to give some answers to upcoming cross-domain questions. Only if OPC UA is able to bridge the gap between several different domains, it will be accepted as real Internet-of-Things protocol and therefore supported even better by the APIs of the big IT players. One technology which already interconnects nearly every domain is the REST architecture. A lot of research already focused on the combination of these two technologies. However, in this paper we will show the first approach which covers all OPC UA requirements and therefore enables also use cases like server reconfiguration. We will also give some insights into our concept, which was contributed to the OPC UA specification as part of the V1.04 release.

Zusammenfassung

Increasing competition in the automotive industrymakes cost-saving integration of more and more vehicle derivatives and variants such as sedans, coupes, cabriolets as well as electrical and combustion engine models into existing production systems necessary. At the same time, the production planners face a major challenge while integrating vehicle bodies-in-white. In contrast to the original concept and rough planning state, the automated production plants are continuously optimized during the detail planning phase as well as after the start of production as a result of improved processes and model upgrading. For fast integration of new vehicles, a current digital image of the real production plant ─ the so-called Digital Twin ─ is groundbreaking. This Digital Twin of a factory consists of a current bill of resources for cost calculation and a current layout planning state. The paper describes a concept for creating a Digital Twin of a body-in-white production system for the concept and rough planning projects. In the internal concept planning phase, planners do cost calculations and layouts for ordering factory suppliers. However, for integration planning, the original concept and rough planning project have to be updated. Therefore, a new concept has been developed which uses current information from the cyber-physical system and a current 3D scan to update the bill of resources and the layout planning on demand.

Zusammenfassung

The increasing change of production leads to differences between the current shop floor and the state of planning. This difference causes significant challenges for production planners while integrating new products into existing production systems. To tackle this issue, this paper presents a concept for the automated creation of a digital twin of a body-in-white production system based on current resources, products as well as process information from the cyber-physical system. The paper focuses on the different data sources and information in cyber-physical systems necessary for integration planning. Furthermore, major parts of the concept are evaluated in a real body-in-white production system. The resulting digital twin enables faster product integration and Industry 4.0 concepts.

Zusammenfassung

Based on autonomous software agents capable of calculating individual numerical field problems, a distributed method for solving transient field problems is presented. The software agents are running on distributed resources connected via a network and represent a dynamic calculation environment. Communication and data exchange between multiple agents enables their collaboration and allows decisions based on distributed overall knowledge. As unique characteristics, no central unit influences the solution process at any time. The presented simulation example and its evaluated calculation process proves the method to benefit from redundant resources.

Zusammenfassung

Increasing product variety and the shortening of product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing automation systems. To face this challenge one solution is a Digital Twin, which can be used to reduce the complexity and time of reconfiguration by early detection of design or process sequence errors of the system with a cross domain simulation. For engineering the Digital Twin and systemically synchronizing the data of mechatronic components in the interdisciplinary engineering models of a Digital Twin during the life cycle of manufacturing automation systems, this paper presents a concept for the engineering of a Digital Twin based on model integration in a PLM IT Platform and an Anchor Point method to systematically detect variances of the mechatronic data structure between the digital models and the physical system. The data of a mechatronic component from interdisciplinary domains, developed by the corresponding engineering tools are referred to as anchor points. This paper analyses domain specific challenges in automation software code to develop an assistance system for rule based consistency check and for synchronizing the engineering models of the Digital Twin of the manufacturing automation system based on the Anchor Point method.

Zusammenfassung

Heute wird Simulation während der Entwicklung und Inbetriebnahme von Automatisierungssystemen genutzt, allerdings weitet sich die Nutzung von Simulation durch die Einführung des „Digitalen Zwillings“ auch auf den Betrieb aus. Durch diese neuen Einsatzmöglichkeiten der Simulation ergeben sich, neue Herausforderungen, gerade bei der Simulation von IoT-Systemen, da diese dynamische und heterogene Systeme sind. Durch die hohe Dynamik (Ein- und Austreten der Komponenten) und Heterogenität (stark unterschiedliche Komponenten) in IoT-Systemen ist es nicht mehr möglich alle Komponenten mit allen relevanten Aspekten in einem Simulationstool zu simulieren, weshalb eine Co-Simulation benötigt wird. Um der hohen Dynamik des realen Systems gerecht zu werden, wird auch ein Konzept benötigt um „Plug-and-Simulate“ zu ermöglichen. In diesem Beitrag werden zuerst existierende Ansätze zur Simulation von IoT-Systemen sowie zur Co-Simulation vorgestellt und auf ihre Tauglichkeit hinsichtlich der Simulation von heterogenen und dynamischen IoT-Systemen untersucht. Anschließend wird ein neuartiges Konzept vorgestellt, das ein Eintreten von neuen, zur Entwurfszeit unbekannten Modellen zur Laufzeit der Simulation in eine Co-Simulation ermöglicht. Abschließend wird eine prototypische Umsetzung des Konzepts am Beispiel der Co-Simulation eines IoT-Warenlagers vorgestellt.

Zusammenfassung

In the development of automated systems in the field of industrial automation, engineers often have to work on many tasks under time pressure, including simulations. In order to support engineers in these complex simulation tasks, a framework is presented which coordinates and calculates simulations based on structured knowledge in a parallelized, decentralized way. Different approaches for a problem are simulated automatically and compared with each other. Therefore, complex problems, meaning large-scale ones that often consist of coupled problem parts, are decomposed into partial problems. Their approaches are subsequently processed separately each using the most suitable software tool. Cooperating software agents are utilized to achieve a flexible coordination of decentralized simulations. These autonomous software units communicate with each other to control the parallelized activities during a simulation project. The presented concept is evaluated based on the application fields of control design and multiphysics simulation. Hence, multi-agent systems (MAS) were generated prototypically using the developed framework. As an addition to the framework for parallel simulation, a concept for supplementing the MAS with a case-based reasoning component is presented. Using the case-based reasoning addition, the performance of the simulations was further improved. Due to the varying computation time of the different approaches, analyzed in parallel, results are often available earlier. The MAS is also capable of improving the quality of the results because of the automatic analysis and comparison of different approaches.

Zusammenfassung

Um Ingenieure bei Simulationsaufgaben zu unterstützen, wird ein Framework vorgestellt, das Simulationen basierend auf strukturiertem Wissen parallel und dezentral berechnet. Zur flexiblen Koordination der dezentralen Simulationen werden Softwareagenten eingesetzt. Evaluiert wird das vorgestellte System anhand des Anwendungsgebiets des Regelungsentwurfs.

Zusammenfassung

Aufgrund des zunehmenden Wettbewerbs und der Elektrifizierung in der Automobilindustrie werden zukünftig immer mehr Fahrzeugderivate und -varianten kostensensibel in bestehende Produktionslinien integriert. Gleichzeitig stehen die Produktionsplaner bei einer Fahrzeugintegration in den Karosserierohbau vor einer großen Herausforderung. Denn im Gegensatz zum ursprünglichen Planungsstand werden die automatisierten Produktionsanlagen nach dem Start der Produktion infolge von Verbesserungsprozessen kontinuierlich optimiert. Für eine realistische Integrationsplanung ist deshalb ein aktuelles, digitales Abbild der Produktionsanlage, auch Digitaler Zwilling genannt, wegweisend. Aufgrund der Vielzahl von Produktionseinrichtungen in Großunternehmen und des Wandels in der Produktion ist die Identifizierung von den aktuell vorhandenen Geräten eine Grundvoraussetzung, um einen Digitalen Zwilling erzeugen zu können. Hierfür werden zwei unterschiedliche Methoden zur Informationsbeschaffung und Identifizierung von aktuell vorhandenen Geräten in einem cyber-physischen System in der Produktion vorgestellt, verglichen und bewertet. Darauf aufbauend wird ein Verfahren beschrieben, mit dem ein Planungsmodell durch methodisches Mapping mit Automatisierungskomponenten in einer Produktionslinie auf seine Aktualität geprüft werden kann. Das Mapping wird durch das Herstellen einer Beziehung zwischen den elektrischen Geräten und den technischen Ressourcenbeschreibungen in den Planungsmodellen erreicht. Es ermöglicht eine Überprüfung, ob Ressourcen in der Produktion gegenüber dem Planungsstand neu hinzugekommen oder zurückgebaut wurden. Somit können Änderungen in Bereichen oder Teilbereichen von Planungsmodellen ermittelt werden. Darauf aufbauend wird in diesem Beitrag erstmals ein Mapping und Zusammenführen des Mengengerüstes eines Planungsmodells und des Mengengerüstes aktueller elektrischer Geräte aus der Produktion auf Basis des Datenformates AutomationML untersucht und anhand von Datensätzen aus der Praxis verifiziert. Das standardisierte Datenformat AutomationML etabliert sich derzeit in der Automobilindustrie und eignet sich zum systemunabhängigen Datenaustausch zwischen verschiedenen Softwarewerkzeugen. In diesem Beitrag wird aufgezeigt, inwieweit es Standards ermöglichen, Geräte im cyber-physischen System den Planungsmodellen der Digitalen Fabrik zuzuordnen. Gleichzeitig werden die praktische Anwendbarkeit und die Grenzen einer derartigen Methode anhand einer realen Produktionsanlage eines vollautomatisierten Karosserierohbaus in der Automobilindustrie verifiziert.

Zusammenfassung

Produzierende Unternehmen stehen stets vor der Herausforderung, qualitativ hochwertige Produkte zu erzeugen, die den strengen Anforderungen der Kunden und des Gesetzgebers genügen. Das Qualitätsergebnis muss trotz entlang der Wertschöpfungskette auftretender Schwankungen und Unregelmäßigkeiten reproduzierbar sein. Das gilt speziell für die Branche der Massivumformung, die durch ihre Abhängigkeit von den Entwicklungen in der Automobil- sowie Luft- und Raumfahrtindustrie zusätzliche Anforderungen durch den dort stattfindenden strukturellen Wandel hin zur E-Mobilität und zum Leichtbau erfährt. Um diesen Herausforderungen gewachsen zu sein, werden Lösungen benötigt, die die zunehmende Komplexität entlang der Prozesskette beherrschbar machen und zu einer erhöhten Prozessstabilität und Effizienz beitragen. Dieser Beitrag stellt einen datengetriebenen Ansatz vor, der darauf abzielt, kurzfristig auftretende Qualitätsschwankungen zu verfolgen. Prozessdaten werden dazu In-Memory in einer multidimensionalen Datenbank modelliert. Auf Basis der Ergebnisse eines Online Analytical Processing (OLAP) soll in Echtzeit reagiert und steuernd auf die Prozesskette eingewirkt werden.

Zusammenfassung

Modern production systems are increasingly interconnected and flexible and therefore form Internet-of-Things-systems (IoT). Because of the flexibility those systems are also dynamic, meaning the entering and leaving of components during runtime, and heterogeneous. For the simulation of such systems, those challenges of dynamic and heterogeneity have to be met. Therefore the authors presented an agent-based co-simulation concept, but an important aspect of a co-simulation is the synchronization of the used simulations, which hasn’t been considered yet. In this contribution the challenges of synchronizing a co-simulation of IoT-systems are introduced and existing co-simulation synchronization concepts examined with regard to their usability for simulating IoT-systems. Afterwards a synchronization concept is presented, which can be used in the presented agent-based co-simulation concept.

Zusammenfassung

Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. A better understanding of the system’s behavior with the aid of data is the key to improve reliability and process stability. Current data analytics approaches show decent results concerning the optimization of single processes but lack in extensibility to plants with high-dimensional data spaces. This paper presents and compares two data-driven self-learning approaches that are used to detect anomalies within large amounts of machine and process data. Models of the machine behavior are generated to capture complex interdependencies and to extract features that represent anomalies. The approaches are tested and evaluated on the basis of real industrial data from a metal forming process.

Zusammenfassung

Automated manufacturing machines in the discrete manufacturing domain are frequently facing changes in environmental conditions such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical and software changes. In industrial practice, these adaptation processes are individually performed by experts without methodological support which is time-consuming and highly error-prone. This article proposes a structured approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

Zusammenfassung

Unerwartete Ausfälle von Anlagenkomponenten sowie unvorhersehbare Prozessereignisse und Anomalien sind Treiber erhöhter Ineffizienzen in Form von Stillstandzeiten und schwankender Produktqualität. Produzierende Unternehmen stehen vor der Herausforderung, trotz entlang der gesamten Wertschöpfungskette auftretender Dissonanzen, ein hochwertiges und reproduzierbares Qualitätsergebnis zu gewährleisten. Dazu werden Lösungen benötigt, die die zunehmende Komplexität entlang vernetzter und hochgradig dynamischer Prozessketten beherrschbar machen. Intelligente Sensor-Netzwerke erlauben den Aufbau umfangreicher Infrastrukturen zur Datenerfassung und sind Wegbereiter für den Einzug datenanalytischer Konzepte und Lösungen in die Fertigungsautomatisierung (‘Big Data Analytics’). In diesem Beitrag wird ein datengetriebener Ansatz zur Qualitätsoptimierung vorgestellt, auf dessen Basis Interdependenzen entlang von Prozessketten erfasst und mit Hilfe von multidimensionalen Datenmodellen abgebildet werden können. Aufgrund der Tatsache, dass aktuelle ‘Self-Learning’-Ansätze potenzielle Interdependenzen zwischen Prozessen nicht adäquat modellieren, mangelt es diesen aufgrund der isolierten Optimierung von Einzelprozessen an Skalierbarkeit. Systeme und Prozessketten mit hochdimensionalen Datenräumen können dadurch nicht oder nur unzureichend abgebildet werden. Durch eine verteilte, multidimensionale Modellierung ist dagegen die Integration multipler Prozessschritte in die analytische Betrachtung möglich. Das in dieser Arbeit beschriebene Konzept stellt darüber hinaus einen „Grey-Box-Ansatz“ dar, indem es vorsieht, bestehendes Wissen aus dem Engineering um aus Daten extrahierte Modellstrukturen zu erweitern und zu einem ganzheitlichen Modell zusammenzuführen. Darauf aufbauend wird das in der vorliegenden Arbeit propagierte Gesamtsystem durch zwei zusammenwirkende Rückkopplungsschleifen realisiert. Bestehendes Wissen wird in einer direkt steuernd auf die Prozesskette einwirkenden Schleife mit Hilfe von Regelstrukturen abgebildet, die die verteilten, multidimensionalen Datenmodelle semantisch verknüpfen. Diese wird um eine vollständig datengetriebene Schleife ergänzt, die die Dimension des erfassten Datenraumes reduziert, Merkmale auf Basis maschinellen Lernens extrahiert und die Wissensbasis kontinuierlich erweitert. Dadurch wird eine ganzheitliche und datenbasierte Qualitätsoptimierung realisiert. Der Ansatz wird momentan prototypisch für eine Modell-Prozesskette der Massivumformung unter Laborbedingungen sowie für zwei Pilotanlagen im realen industriellen Umfeld der Unternehmen Otto Fuchs KG und Hirschvogel Automotive Group umgesetzt. In diesem Beitrag wird die Datenextraktion und -aufbereitung, die prototypisch umgesetzte Datenmodellierung, der Ansatz zur Datenanalyse sowie das Gesamtkonzept vorgestellt.

Zusammenfassung

The increasing complexity of distributed automation systems requires new methods to verify the correct functionality. Modelbased verification is an established approach to test the behavior of the system under test, before going into operation. To apply model-based techniques the overall system model of the automation system is needed. Due to the high complexity of the overall system and the changing dependencies caused by reconfigurations or software modifications an overall system model is seldom available or maintained. In this paper, we propose a modeling technique to manage the complexity of the overall system by modularization which is dedicated to distributed systems. This is presented in a formal way. Thereby, the modeling techniques regard the requirements of a service-oriented-architecture and the properties of automation systems, like interfaces to the technical process and parallelism caused by the distribution. In addition, we present calculation rules how to build up the overall system model automatically which can be used to verify system requirements.

Zusammenfassung

Die Digitalisierung im Bereich der industriellen Automatisierungstechnik und die damit einhergehenden Veränderungen bei der Produktentwicklung und im Betrieb haben weitreichende Folgen. Ziel des vorliegenden Statusreports ist es, die Auswirkungen der Digitalisierung auf das Testen von Industrie-4.0-Komponenten und -Systemen zu beschreiben.

Zusammenfassung

Änderungen der Funktionalität von Automatisierungssystemen werden zunehmend an der Software durchgeführt. Insbesondere bei verteilten Automatisierungssystemen lassen sich die Auswirkungen von Software-Änderungen oftmals nur schwer abschätzen. Dies stellt insbesondere Anlagenbetreiber vor große Herausforderungen bei der Absicherung von Funktions-Änderungen in der Betriebsphase. Dieser Beitrag beschreibt einen modellbasierten Ansatz, mit dem Auswirkungen von Software-Änderungen erkannt werden können und das betroffene Teilsystem automatisiert über modellbasierte Verifikationsverfahren abgesichert werden kann. Das präsentierte Konzept beruht auf der Komposition eines Systemmodells aus diskreten Verhaltensmodellen der Komponenten des Automatisierungssystems. Abschließend wird das Konzept anhand unterschiedlicher Änderungsszenarien evaluiert.

Zusammenfassung

Automated manufacturing machines in the discrete manufacturing domain frequently face changes in requirements, such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical, and software changes. In industrial practice, experts individually perform these adaptation processes without methodological support, which is time-consuming and highly error-prone. This article proposes a systematic approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

Zusammenfassung

In diesem Beitrag wird eine dezentral aufgebaute Methodik zur integrierten System- und Layoutplanung vorgestellt. Das Konzept für die Systemplanung basiert auf intelligenten Planungsobjekten, welche die autonomen Einheiten innerhalb der Planung logistischer Systeme darstellen. Diese intelligenten Planungsobjekte werden in einem agentenbasierten Assistenzsystem abgebildet. Basierend auf den intelligenten Planungsobjekten ist das Systembildungskonzept in die Phasen Komposition, Konfiguration und Adaption unterteilt. Diese werden als regelbasierte Interaktionen zwischen den intelligenten Planungseinheiten modelliert. Das Konzept der integrierten Layoutplanung basiert auf der Verwendung eines mathematischen Modells. Hierzu wurden entsprechende Agenten für die Layoutplanung konzipiert. Das im mathematischen Modell beschriebene Optimierungsproblem wird dabei mit Hilfe einer Heuristik gelöst. Ein Agentensystem organisiert die Ausführung des Heuristik-Algorithmus für alle möglichen Layout-Konstellationen und bewertet diese anschließend. So bietet es, in Form eines Assistenzsystems, Unterstützung von der Aufgabenstellung über die Ressourcenauswahl bis hin zur Layoutplanung.

Zusammenfassung

Global trends in manufacturing urge existing machines to become more flexible, as the production environment becomes increasingly dynamic. In order to attain this “flexibilization”, existing machines have to be adapted during their operational phase, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof have to be performed. The adaptation of existing machines is a complicated and time-consuming process, as various interdependencies have to be considered. Therefore, a systematic approach for generating adaptation options is crucial for a successful adaptation of machines. State-of-the-art approaches primarily focus on the adaptation of products and, thus, are not appropriate for manufacturing machines. Accordingly, this paper presents an approach for the generation of adaptation options to support the adaptation of manufacturing machines. Furthermore, a use case study is presented and discussed, where the approach is applied to an industrial manufacturing machine. Here, the results demonstrated that the approach is applicable for an industrial manufacturing machine and can automatically generate valid adaptation options.

Zusammenfassung

Tracking each workpiece provides two major advantages in forging technology. First, the matching of physical workpiece with the monitored process information facilitates root‐cause analysis for product quality. Second, the following process steps can be adapted according to the incoming workpiece properties to improve the robustness of hot forging process chain. The paper presents a general tracking methodology and tagging experiments on aluminium and steel forgings for harsh drop‐forging technology. Furthermore, a framework for streaming and processing large amounts of real‐time data as well as a multidimensional approach to model and analyse the workpiece information for individual and batch‐tracking are presented.

Zusammenfassung

The environment of todays automated manufacturing machines is highly dynamic. Unpredictable changes in customer demands and shorter product lifecycles pose challenges for automated manufacturing systems which usually are designed for a static context. Thus, flexibility and changeability are discussed in order to face the challenges that arise from the dynamic production environment. This paper presents the results of two literature reviews on flexibility indicators and changeability indicators and discusses the determined indicators as well as their usage in the literature over time. A classification of the indicators based on their frequency of usage is also given.

Zusammenfassung

Reduction in use of conventional oil-lubricates in drawing processes has been important working aims of many investigations in sheet metal forming from past until today. A completely new approach is developed at the University of Stuttgart, using volatile media like N2 or CO2 as lubrication for sheet metal deep drawing processes. Doing so, remarkable results were obtained regarding friction behaviour of this new tribological system. Sustainability, environmental protection and economic efficiency are the most outstanding advantages of this new process compared to conventional oil based lubrication. The paper includes perspective results from fundamental investigations friction investigations to testing and validating the applicability of the new process in a deep drawing tool. Tracking of workpieces provides two advantages in forging technology. First, the matching of workpiece with the monitored process information makes the root-cause analysis for product quality possible. Second, the process steps can be adapted based on the incoming workpiece properties to improve the robustness of hot forging process chain. For that purpose, a general tracking methodology was developed and labelling experiments on steel and aluminium parts appropriate for harsh drop-forging environments has been conducted. Furthermore, a multidimensional approach to model and to analyse the workpiece information for individual and batch-tracking as well as a framework for streaming and processing of large amounts of real-time data are presented in this contribution.

Zusammenfassung

Intelligent industrial automation devices based on cyber-physical systems can establish ad-hoc networks of manufacturing systems to produce individual products. To realize those, all network-participants, with their offered services and properties need to be described in an explicit semantic. A cloud-based concept is conceived in this paper which includes a semantic to describe the manufacturing systems, their characterization as well as the specified user requirements. A cloud-based concept called “e-Production” maps the abilities of the manufacturing systems with the customer requirements and identifies the best collaborative units for production regarding to energy, costs and other quality characteristics.

Zusammenfassung

Based on autonomous software agents, capable of calculating individual numerical field problems, a distributed method for solving transient field problems is presented. The software agents are running on distributed resources connected via network and represent a dynamic calculation environment. Communication and data exchange between multiple agents enables their collaboration and allows decisions based on distributed overall knowledge. As unique characteristics, no central unit influences the solution process at any time. A presented simulation and its evaluated calculation process proves the method to benefit from redundant resources and outline its flexibility for non-permanent resources.

Zusammenfassung

Mit steigendem Funktionsumfang erhöht sich die Komplexität von industriellen IT-Systemen. Dabei stellt die verteilte Entwicklung der Systeme und der Einsatz in unterschiedlichen Varianten eine besondere Herausforderung dar. Bei der Koordination des Systemtests fehlt es oft am Abgleich. So stehen oftmals nicht ausreichend Daten zur Verfügung, um einen Überblick über den Testfortschritt zu erhalten. Im Folgenden wird ein Konzept für ein agentenbasiertes Assistenzsystem beschrieben, welches im gesamten Entwicklungsnetzwerk verteilt wird. Damit gelingt es, eine globale Übersicht über den aktuellen Testfortschritt zu erhalten und Testfälle für den Regressionstest zu priorisieren.

Zusammenfassung

A novel approach is presented applying capabilities of software agent-based programming to numerical field computations. Distributed and autonomous calculation resources are used in analogy to a machine-to-machine system application for setting up and solving coupled simulations respecting the individual machine capabilities. Within the presented system, the involved machines interact cooperatively and comparatively. No central unit takes any influence to the solution process. Redundant resources do automatically and proactively add variance to the solution process by modifying their partial tasks based on negotiations between the involved resources. In that way, different numerical methods, solution strategies, and solver configurations are automatically evaluated as examples of the variety within coupled simulations. Partial results are dynamically integrated into an overall solver sequence, demonstrated on the coupled electric field computation of a patch antenna array. A quick provision of results is shown contemplating changes at calculation resource and vague user decisions for a nonlinear and bidirectionally coupled simulation of a 3-D multiphysics problem.

Zusammenfassung

Bei der Bestimmung der Zuverlässigkeit von cyber-physischen Systemen müssen neue Ansätze und Konzepte entwickelt werden, die den Anforderungen an die neuen Technologien gerecht werden. Neben der Möglichkeit, zur Laufzeit erfasste Daten, die durch die Vernetzung von Komponenten zur Verfügung stehen, zur genaueren Berechnung der Zuverlässigkeit zu verwenden, stellt insbesondere die dynamische Systemstruktur von cyber-physischen Systemen, die zum Zeitpunkt der Systementwicklung noch nicht bekannt ist, Herausforderungen und nutzbare Potenziale für die Zuverlässigkeitsanalyse dar. Der vorliegende Beitrag präsentiert ein Konzept zur dynamischen Zuverlässigkeitsberechnung mithilfe eines Agentensystems. Dabei wird die klassischerweise oft als konstant angenommene Ausfallrate in drei zeitabhängige Bestandteile zerlegt, um beispielsweise den Einfluss der menschlichen Zuverlässigkeit in die Berechnung mit aufzunehmen. Mithilfe von Software-Agenten werden die erforderlichen Informationen zur Berechnung der Teilausfallraten ermittelt. Anhand eines Prototyps wird der Einfluss dynamischer Einflussgrößen auf die Zuverlässigkeit eines Systems gezeigt.

Zusammenfassung

Im Beitrag wird ein Konzept vorgestellt, das es ermöglicht, bestehende Automatisierungssysteme für Industrie 4.0 und Internet of Things zu ertüchtigen. Das Hauptaugenmerk des Konzepts liegt auf den Kommunikationsfähigkeiten, die für eine Interaktion der Systeme notwendig sind. Damit diese Fähigkeiten ohne umfassende Modifikationen der Automatisierungssysteme realisiert werden können,besteht das Konzept aus verschiedenen Komponenten, die die Anbindung an das Automatisierungssystem, die Repräsentation desselben und die Kommunikation mit den anderen Automatisierungssystemen übernehmen.

Zusammenfassung

Serviceorientierte Architekturen ermöglichen flexible Steuerungsstrukturen. Allerdings steigt dadurch die Komplexität beim Engineering der Komponenten des Steuerungsnetzwerks. Im Rahmen dieser Veröffentlichung wird aufgezeigt, dass eine modellgetriebene Entwicklung kombiniert mit einer wiederverwendbaren, modular-aufgebauten Systemsoftware den Umgang mit Komplexität beim Engineering vereinfachen kann.

Zusammenfassung

Zur Ermittlung des Systemverhaltens werden beim Engineering in Kombination mit der modellgestützten Entwicklung häufig Simulationen eingesetzt. Dieser Beitrag stellt einen agentenkoordinierten Ansatz für die parallele Simulation von multiphysikalischen Problemstellungen auf heterogenen Rechenressourcen vor. Durch die unterschiedlichen eingesetzten Simulationsmethoden steigt hierbei die Zuverlässigkeit beim Erreichen der Ergebnisse.

Zusammenfassung

A Digital Twin, an always in sync digital model of existing manufacturing cells, can be used to reduce time and risk of reconfiguration by early detection of design or process sequence flaws of the system in virtual commissioning and simulation. In this paper the need of Digital Twins in future production plants as well as the structure of the Digital Twin is presented. The engineering process of production systems is a cross-domain challenge between mechanics, electrics and software, but a lack of collaboration and universal information transfer between the domains leads to a high investment volume by synchronization the digital model from the time of commissioning. To synchronize cross-domain mechatronic data models of mechatronic components in the digital world during the life cycle of existing production systems this paper presents the anchor point method to firstly detect variances of cross-domain mechatronic data structure between the digital model and the real system in the specific domains electrics, mechanics and software and update the virtual models to have a consistent data model of the Digital Twin.

Zusammenfassung

Internet-of-Things-(IoT)-systems will pose new challenges for the simulation of such systems, like the dynamic entering of components during runtime and the huge heterogeneity of the components. Various existing approaches for the simulation of IoT-systems are described. Additionally a concept is presented, which meets the described challenges. To satisfy the heterogeneity each component is modelled individually in separate modelling tools. The concept utilizes a multi-agentsystem to simulate the communication between the different models and to enable them to be integrated during simulation time. With the presented concept it is also possible to integrate real components into the simulation.

Zusammenfassung

Automated manufacturing machines need to evolve in order to compete in nowadays turbulent manufacturing environment. This evolution process is achieved by performing different adaptations of the system, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof. Currently, this adaptation process is mostly based on the experience of experts and is time-consuming and error-prone. This contribution proposes an approach that integrates tool support in the adaptation process to assist the engineer in the overall process. The approach uses a multi agent system in order to analyze production change requests and to generate and evaluate possible adaptation options for the machine based on a mechatronic model of the system including interdependencies between products, processes and resources on parameter-level. Thus, adaptation options can be generated on a fine-grained level. An illustrative example for the concept is given by the application on a modular production system.

Zusammenfassung

Bestehende automatisierte Produktionssysteme sind einem ständigen Wandel unterworfen, da sie an geänderte Rahmenbedingungen angepasst werden müssen. Dies kann durch Änderungen der Hardware und/oder der Software erfolgen, wobei der Änderungszyklus der Software im Allgemeinen kürzer ist. Dabei ist für die Planung der Anlagen-Evolution auch die Abschätzung des durch eine Softwareänderung entstehenden Aufwands von Interesse. Unter Berücksichtigung von SPS-spezifischen Besonderheiten wird in diesem Beitrag das Konzept eines Assistenzsystems vorgestellt, das die Aufwandsabschätzung durch die Kombination von fallbasiertem Schließen mit der Anwendung von Software-Metriken unterstützt. Anschließend wird die Umsetzung des beschriebenen Assistenzsystems sowie eines Tools zur automatisierten Anwendung von Software-Metriken auf SPS-Programme gezeigt.

Zusammenfassung

Due to different trends in manufacturing, existing mechatronic systems, such as automated manufactur-ing machines, are obliged to evolve in order to cope with a quickly changing environment. The evolution of mechatronic systems is achieved by adaptation processes, where changes in the physical structure of a system and/or its automation software are performed. An important phase of an adaptation process is the modeling and analysis of existing interdependencies between system elements which serve as a basis for subsequent adaptation phases. However, current modeling approaches mostly focus on single sys-tem element types and do not regard the holistic system, i.e., products, processes and resources. Accord-ingly, in this contribution a concept for modeling interdependencies between products, processes and re-sources is proposed with the aim of providing a solid data base for the generation of adaption options. The application of the concept is demonstrated by an illustrative example of a pick-and-place unit.

Zusammenfassung

Die zunehmende Produktvielfalt und die Verkürzung der Produktlebenszyklen erfordern eine schnelle und kostengünstige Rekonfiguration bestehender Produktionssysteme. Dafür ist ein aktuelles digitales Modell der bestehenden Fertigungszelle, ein digitaler Zwilling, eine geeignete Lösung. Der digitale Zwilling verringert die Kosten und verkürzt die Umrüstzeiten durch virtuelle Planung und Simulation – basierend auf dem aktuellen Zustand der realen Produktionsanlage und durch eine frühzeitige Erkennung von Konstruktions- oder Prozessablauffehlern in der Produktionsanlage. Voraussetzung ist allerdings, dass ein aktuelles (virtuelles) Anlagenmodell von den mechatronischen Bestandteilen der realen Anlage während der verschiedenen Phasen ihres Lebenszyklus existiert. Im Beitrag wird die domänenübergreifende, mechatronische Datenstruktur virtueller Fertigungszellen in der Automobilindustrie diskutiert. Es wird eine systematische Ankerpunktmethode vorgestellt, mit Hilfe derer sich die Abweichungen zwischen den virtuellen Modellen und der Realität ermitteln lassen. Basierend darauf wird eine regelbasierte Konsistenzprüfung zur durchgängigen, domänenübergreifenden Synchronisierung der aktuellen mechatronischen Ressourcenkomponenten der Produktionssysteme mit deren virtuellem Anlagemodell vorgestellt.

Zusammenfassung

Die zunehmende Produktvielfalt und die Verkürzung der Produktlebenszyklen erfordern eine schnelle und kostengünstige Rekonfiguration bestehender Produktionssysteme 1. Um die- sen Herausforderungen zu begegnen, ist ein aktuelles digitales Modell der bestehenden Fer- tigungszelle, im Folgenden Digitaler Zwilling genannt, eine geeignete Lösung. Der Digitale Zwilling führt zu einer Kostenreduktion durch Verkürzung der Umrüstzeiten durch virtuelle Planung und Simulation basierend auf dem aktuellen Zustand der realen Produktionsanlage als auch durch eine frühzeitige Erkennung von Konstruktions- oder Prozessablauffehlern in der Produktionsanlage. Voraussetzung für die Verwendbarkeit des Digitalen Zwillings vom Produktionssystem ist allerdings, dass ein ak tuelles (virtuelles) Anlagenmodell von den me- chatronischen Bestandteilen der realen Anlage während der verschiedenen Phasen ihres Lebenszyklus existiert. In diesem Beitrag wird die domänenübergreifende, mechatronische Datenstruktur der virtuellen Fertigungszellen in der Automobilindustrie diskutiert. Es wird eine systematische Ankerpunktmethode vorgestellt, mithilfe derer die Abweichungen zwischen den virtuellen Modellen und der Realität detektiert und ermittelt werden können. Basierend darauf wird eine sogenannte regelbasierte Konsistenzprüfung zur durchgängigen, domänen- übergreifenden Synchronisierung der aktuellen mechatronischen Ressourcenkomponenten der Produktionssysteme mit deren virtuellem Anlagemodell vorgestellt.

Zusammenfassung

Cyber-physical production systems based on technologies such as machine to machine communication, the Internet of Things and other cutting edge technologies are going to advance manufacturing automation and industrial production. Information technology seems once again to be the driving force for change in manufacturing automation. But what are the characteristics of such systems in comparison to the existing approaches? In this article we recommend an evaluation model for cyber-physical production systems is proposed based on a set of system characteristics, which defines specific abilities and performance indicators. Furthermore, an analysis and verification of that model is presented sketching the typical pattern and impact of cyber-physical production systems. As a result a refined evaluation model is available, suitable for the characterization of cyber-physical technologies and thereby enabling a technological assessment.

Zusammenfassung

Sharing knowledge and experience between test and diagnosis of production systems is a key to reduce faults and increase the availability of the system. In distributed productions with several factories on different locations, the knowledge sharing becomes an important issue because direct communication between the test and maintenance teams is hardly possible. Based on test and diagnosis of an industrial quality assurance system we propose a combined decision support system for test and diagnosis that automatically shares the knowledge between both support functions. Using a decision support system motivates users to properly formalize their knowledge and reduces communication overhead. Maintainers are supported by an automatic fault diagnosis based on fault symptoms. Testers are supported by a test progress overview and a test case prioritization. By linking test cases to fault symptoms, the diagnosis can be enriched by a test case recommendation for further fault isolation and the test case prioritization is expanded by a fault symptom coverage. As demonstrated with the industrial quality assurance system, both leads to synergy effects that improve the support functions and reduce communication.

Zusammenfassung

Wheeled walkers are recommended to improve walking performance in older persons and to encourage and assist participation in daily life. Nevertheless,using a wheeled walker can cause serious problems in the natural environment. This study aimed to compare uphill and downhill walking with walking level in geriatric patients using a wheeled walker. Furthermore, we investigated the effect of using a wheeled walker with respect to dual tasking whenwalking level.

Zusammenfassung

Due to current trends in manufacturing, existing machines need to become more flexible to cope with a more dynamical production environment. To achieve this“flexibilization”, existing machines have to undergo adaptation processes during their operational phase, i.e. mechanical changes, changes of sensors and actuators, software changes, or a combination thereof. A fundamental part of these adaptation processes is the generation of adaptation options. Here, many constraints and interrelations must be considered, which complicate these processes and may result in suboptimal or error-prone results. Therefore, a systematical and efficient approach for generating adaptation options is indispensable for a successful adaptation process. State-of-the-art approaches mostly focus on the adaptation of products and are not appropriate for manufacturing machines. Accordingly, in this contribution an approach for the generation of adaptation options is proposed to support the adaptation of manufacturing machines. The approach is based on the interdependencies between products, processes, and resources. The application of the approach is demonstrated by an illustrative example of a pick-and-place unit.

Zusammenfassung

Die Digitalisierung in der akademischen Bildung wird in der Studie aus der organisationalen Perspektive einer Hochschule - als „Modell“ – betrachtet und konkretisiert anhand der Fallbeispiele des Karlsruher Instituts für Technologie und der Universität Stuttgart. Als wesentliche Einflussbereiche gelten die digitale Durchdringung der Wissenschaft und des Studiums, die digitale Vernetzung der hochschulischen Systeme sowie die gewohnheitsmäßige Nutzung digitaler Medien bei Lehrenden und Lernenden.

Zusammenfassung

This paper presents the latest development towards a software agent–based numerical framework for interdisciplinary coupled problems. The software agent framework is capable to incorporate multiple physical effects and combine multiple numerical methods through the usage of a neutral abstraction layer. The principles of software agent–based programming and the application in a numerical example are discussed. This paper focuses on dielectric heating and different methods for solving the time harmonic Maxwell equation. By reusing state of the art finite element method (FEM) and boundary element method (BEM) software tools and managing the interface between the different tools, this approach solves complicated and realistic three-dimensional problems. The functionality and efficiency of the framework are demonstrated on the dielectric heating inside a human head due to electromagnetic waves. A proper iterative coupling formulation for FEM and BEM is derived and numerically validated. The FEM-BEM coupling separates from existing approaches, because it takes advantage of new developments in the FEM, namely, the perfect handling of infinite domains through perfectly matched layers. Moreover, the simplicity and flexibility of the coupling method allow implementation in existing numerical codes.

Zusammenfassung

Produzierende Unternehmen stehen stets vor der Herausforderung, qualitativ hochwertige Produkte zu erzeugen, die den strengen Anforderungen der Kunden und des Gesetzgebers genügen. Das Qualitätsergebnis muss trotz entlang der Wertschöpfungskette auftretender Schwankungen und Unregelmäßigkeiten reproduzierbar sein. Das gilt speziell für die Branche der Massivumformung, die durch ihre Abhängigkeit von den Entwicklungen in der Automobil- sowie Luft- und Raumfahrtindustrie zusätzliche Anforderungen durch den dort stattfindenden strukturellen Wandel hin zur E-Mobilität und zum Leichtbau erfährt. Um diesen Herausforderungen gewachsen zu sein, werden Lösungen benötigt, die die zunehmende Komplexität entlang der Prozesskette beherrschbar machen und zu einer erhöhten Prozessstabilität und Effizienz beitragen. Dieser Beitrag stellt einen datengetriebenen Ansatz vor, der darauf abzielt, kurzfristig auftretende Qualitätsschwankungen zu verfolgen. Prozessdaten werden dazu In-Memory in einer multidimensionalen Datenbank modelliert. Auf Basis der Ergebnisse eines Online Analytical Processing (OLAP) soll in Echtzeit reagiert und steuernd auf die Prozesskette eingewirkt werden.

Zusammenfassung

Aufgrund von vielen Freiheitsgraden bei der Entwicklung von automatisierten Systemen ergibt sich eine hohe Anzahl möglicher Entwicklungsvarianten. Da die Entwicklung ein zeitaufwendiger, iterativer Prozess ist, die Entwicklungszeiten aber aufgrund des Kostendrucks sinken müssen, kann der Ingenieur nur eine sehr begrenzte Anzahl dieser Entwicklungsvarianten berücksichtigen und entwickeln. Die Erstellung der betrachteten Entwicklungsvarianten ist daher heute stark vom Wissen und von den Erfahrungen des Ingenieurs abhängig. Dies führt bereits zu Beginn der Entwicklung zu einer Einschränkung der zu entwickelnden Varianten, ohne dass diese für andere Personen zwingend nachvollziehbar ist. In diesem Beitrag wird ein Konzept für ein agentenbasiertes Assistenzsystem vorgestellt, mit dessen Hilfe der Ingenieur alle Varianten bei der Entwicklung berücksichtigen kann. Die Grundlage für dieses Konzept bilden Komponenten, die als intelligente Planungsobjekte modelliert werden. Ein automatisiertes System wird hierbei als ein Zusammenschluss aus unterschiedlichen Komponenten wie Motor, Kabine etc. betrachtet, die unterschiedliche Funktionen wie beispielsweise Fördern erfüllen. Der Zusammenschluss aus den Komponenten erfüllt die geforderte Gesamtfunktionalität.

Zusammenfassung

Im Rahmen dieser Veröffentlichung wird die Herausforderung der Komplexität verteilter Systeme thematisiert und ein Ansatz zur Komplexitätsbeherrschung vorgestellt. Auf Basis einer mikrocontrollerbasierten Softwarearchitektur wird eine modellgetriebene Entwicklung umgesetzt. Die gekapselten Modelle bilden dabei die Grundlage einer serviceorientierten Architektur.

Zusammenfassung

In this document we describe an approach to interconnect existing industrial automation systems with cooperation networks, like the internet of things, even if the industrial automation system uses a different communication protocol. To realize this, we have developed the concept of connector which provides system-dependent interfaces for different industrial automation and a universal interface for the cooperation network. Between these interfaces the messages are processed and translated by services, which act as a representation of the industrial automation system.

Zusammenfassung

A novel approach is presented applying capabilities of software agent based programming to numerical field computations. Distributed and autonomous calculation resources are used in analogy to a machine to machine system application for setting up and solve coupled simulations respecting the individual machine capabilities. Within the presented system the involved machines interact cooperatively and comparatively. Redundant resources do automatically and proactively add variance to the solution process by modifying their partial tasks based on negotiations between the involved resources. In that way different numerical methods, solution strategies and solver configurations are automatically evaluated as examples of the variety of coupled simulations and dynamically integrated into an overall solver sequence shown for the coupled electric field computation of a patch antenna array. A quick provision of results contemplating changes at calculation resource and vague user decisions is shown for a nonlinear and bi-directionally coupled simulation of a 3d multiphysics problem.

Zusammenfassung

Die Verknüpfung Smarter Komponenten zu dezentral koordinierten IoT-Netzwerken bietet neue Chancen für die Produktionsautomatisierung. Allerdings ergeben sich neue Herausforderungen und ein Bedarf an Konzepten für das durchgängige Engineering, die Integration bestehender Komponenten und derer Absicherung für einen verlässlichen Betrieb.

Zusammenfassung

Im Beitrag wird das Konzept einer flexiblen Gatewayschnittstelle vorgestellt. Dieses Konzept ermöglicht es, ein Gateway mit unterschiedlichen Bussystemen zu verbinden, ohne dass hierzu die Systemsoftware des Gateways angepasst werden muss. Diese Flexibilität wird dadurch erreicht, dass die Schnittstelle und die notwendigen Nachrichten in einer Ontologie beschrieben werden, welche dann maschinell ausgewertet wird. Hierdurch können auch komplexere Funktionalitäten realisiert werden, wozu das Versenden mehrerer Nachrichten notwendig ist. Validiert wurde das Konzept anhand eines Gastronomie-Kaffeevollautomaten mit einem integrierten CAN-Bus. Hierbei konnten nicht nur einfache Funktionalitäten angesprochen werden, sondern auch komplexe Abläufe aus verschiedenen Nachrichten und Parametern realisiert werden sowie neue Funktionalitäten zur Laufzeit hinzugefügt werden.

Zusammenfassung

A novel approach is presented using software agents for an iterative and distributed solution of multiphysics problems. Total convergence is reached by combining individual functionalities of different agents within a multi agent system. Single physics agents cooperatively solve the problem based on specialized, commercial or in-house code. The autonomy of each agent grants a problem dependent solving behaviour without the requirement of a predefined solver sequence. As numerical example, a coupled 3D electromagnetic wave propagation and heat transfer problem inside a waveguide is considered. The required three software agents are described in detail. The created framework solves multiphysics simulations coupled by field values or values exchanged on discrete ports. Here we focus at weak unidirectional and bidirectional field coupled multiphysics problems.

Zusammenfassung

Das hier vorgestellte Konzept zeigt eine Möglichkeit, Verfahren in der Entwicklung von automatisierten Systemen intelligent zu parallelisieren. Dies wird an den Beispielen der multiphysikalischen Simulation und des Regelungsentwurfs gezeigt. Oft sind viele verschiedene alternative Lösungsverfahren für eine Problemstellung bekannt. Zu entscheiden, welches das beste ist, ist zu Beginn eines Projekts meist nicht möglich. Die Zeit, diese Verfahren sequenziell zu untersuchen, hat ein Ingenieur durch die immer kürzeren Entwicklungszyklen, die nötig sind, um sich auf dem Markt zu behaupten, nicht. Das hier vorgestellte Agentensystem unterstützt den Ingenieur, indem es verschiedene Alternativen zur Lösung der jeweiligen Problemstellung untersucht, vergleicht und dem Nutzer die besten Lösungen präsentiert. Durch den Einsatz von Agenten, die miteinander kooperieren, können neben der Bewältigung von Abhängigkeiten auch Synergieeffekte zwischen den alternativen Lösungsmethoden erzielt werden.

Zusammenfassung

The Internet of Things (IoT) is about innovative functionality and better productivity by seamlessly connecting devices. But a major threat is the lack of architecture standards for the industrial Internet and connectivity in the IoT. This article reviews recent IoT architecture evolution and what it means for industry projects.

Zusammenfassung

Die Ziele des Internets der Dinge (IoT) sind die IT-Vernetzung von Gegenständen und die Bereitstellung von Funktionalitäten beziehungsweise Dienstleistungen, die es so noch nie gab. Eine große Schwierigkeit bildet allerdings der Mangel an Referenzarchitekturen und Standards für das neue „Industrial Internet“ und damit die Vernetzung im Internet der Dinge. Dieser Artikel gibt einen Überblick über aktuelle Betrachtungsmodelle, Referenzarchitekturen und ihre Bedeutung für Industrieprojekte.

Zusammenfassung

Future automated systems are connected to each other in a production network with the goal to manage, schedule and operate the manufacturing process of individual products autonomously. Therefore a semantic language is necessary to empower the systems to fulfil their tasks. In this content eCl@ss is often mentioned as applicable to describe products. In this paper we conduct a study to describe an intelligent barrel with eCl@ss in terms of completeness in objective and functional description to evaluate if it is sufficient for the needs of cyber physical systems (CPS).

Zusammenfassung

Modern manufacturing systems based on cyber-physical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.

Zusammenfassung

Industrie 4.0 verursacht aufgrund rekonfigurierbarer Produktionssysteme einen disruptiven Wandel des Qualitätsbegriffs. Die scharfe Trennung zwischen Test (in der Engineering- und Inbetriebnahmephase) und Qualitätssicherung (in der Betriebsphase) löst sich auf. Verursacht durch Rekonfigurationen in der Betriebsphase müssen betroffene Produktionssysteme erneut abgesichert werden. Insbesondere während des Betriebs kommt einem effizienten Test eine herausragende Rolle zu, da durch minimalen Testaufwand der Betrieb möglichst wenig beeinträchtigt und gefährdet werden soll. In diesem Beitrag wird ein Verfahren vorgestellt, das, im Gegensatz zu konventionellen Verfahren, die Absicherung in der Betriebsphase betrachtet. Dabei werden explizit zukünftige Rahmenbedingungen der Produktion, wie Rekonfigurierbarkeit und verteilte Netzwerkarchitekturen, berücksichtigt.

Zusammenfassung

Systeme zur industriellen Produktion in einer Smart Factory arbeiten mehr und mehr daten- und informationsorientiert. Exemplarisch dafür stehen bereits heute Qualitätssicherungssysteme, deren Funktionsbasis in der Regel durch eine komplexe IT-Infrastruktur mit aufwändiger Datenlogistik gebildet wird. Dieser Beitrag stellt ein Assistenzsystem vor, das das Fehlermanagement von solchen Qualitätssicherungssystemen unterstützt, indem es eine automatisierte Fehlerdiagnose mit einer Unterstützung bei der Absicherung von Neuerungen und Änderungen, zum Beispiel nach einer Fehlerbehebung, kombiniert. Grundlage der Assistenzfunktionen bilden eine Datenintegration und Datenauswertung. In dem entwickelten Verfahren werden unterschiedliche Datenquellen erfasst und mit Systemwissen verknüpft, das von Experten abgefragt wird.

Zusammenfassung

The increasing complexity of software in production systems leads to a growing relevance of testing. This trend will continue due to the flexibilization, and IT networking of production systems. This paper proposes a discussion about the requirements of future production systems which will be reconfigurable, decentralized controlled and will consist of autonomous components. From these predicted requirements, new challenges for functional testing are derived. To elucidate these challenges, three use cases illustrate when testing measures will be necessary within the operation phase of production systems.

Zusammenfassung

This paper addresses the issues related to reliability of factory automation applications in the context of Industry 4.0 and proposes a method to calculate it. Factory automation application reliability is a crucial aspect that can influence the entire system, hence it deserves special attention. Current existing methods calculate reliability based on empirical data in a static manner. However, Industry 4.0 acquires consideration of system dynamic behavior. Therefore, this paper presents in addition the influencing factors on reliability of the system and consider its dynamic behavior. The proposed approach was evaluated using a prototype which is included in this paper.

Zusammenfassung

Vernetzte und smarte Anwendungen haben besondere Merkmale wie dynamische Architekturen, verteilte und flexible Kooperation von intelligenten Einheiten. Die Zuverlässigkeit dieser Systeme ist ein signifikanter Faktor für den Erfolg dieser Anwendungen. Die klassischen Zuverlässigkeitsmethoden können nicht ohne weiteres übernommen werden. Es besteht Bedarf an neuen Ansätzen zur dynamischen Berechnung der Zuverlässigkeit. Dadurch können einerseits präventive Maßnahmen zur Erhöhung der Verfügbarkeit solcher Anwendungen abgeleitet werden, andererseits kann nachgewiesen werden, dass die Anwendung die geforderte Zuverlässigkeit auch erfüllt. Dieser Beitrag präsentiert einen Ansatz zur dynamischen Zuverlässigkeitsberechnung von vernetzten kooperierenden Produktionssystemen.

Zusammenfassung

This paper presents a method to describe and increase the software quality of automation systems by a quantitative determination of the software reliability. Therefore, the software development process has been analyzed to identify the essential factors influencing the software reliability. These are the usage of a procedure model, the test factor and the human factor of the software developers themselves. These factors have been considered to create a neuro-fuzzy-based concept, which characterizes and consolidates them to realize an estimation of the software quality. The possibility to estimate the software quality, especially dynamically already during the software development process, will entail a huge benefit to programmers and manufacturers. Effects, methods or circumstances influencing the software quality in a negative or positive way can be numerically described and rated. On basis of that, necessary optimizations in the development process can be performed in time to guarantee a high quality of the software.

Zusammenfassung

Modern manufacturing systems based on cyberphysical systems with a growing amount of software allow frequent updates and reconfigurations to adapt the systems to volatile usage scenarios in the production. A diverse system environment arises even for similar or equal subsystems based on the same platform used at different locations. A major challenge for such systems is the regression test after changes or updates. The resources for the regression test, in a dedicated test environment or deployed to the assembly lines, are limited. To plan the test in the best possible way, a lot of dependencies, relationships and experiences from former tests and tests from other locations have to be considered. This paper describes an assistance system which supports the planning of the regression test in such distributed manufacturing scenarios by combining manual modeling with automated data processing. Therefore the system calculates a cross-location test progress and suggests a prioritized test case sequence.

Zusammenfassung

The increasing cost pressure on Intra-Logistics systems due to the global competition in Intra-Logistics systems motivates planners to decrease planning durations in order to stay cost efficient. Conversely, the rough planning of Intra-Logistics systems is a time consuming, complex task, whose progress is primarily driven by the experience of the planning team. Consequently, the planning is opaque. In order to reduce the planning duration and to systemize the planning this paper presents a rule-based concept for an agent-based assistance system to support the planner during the rough planning of Intra-Logistics systems. The assistance system provides an overview of possible solutions regarding to specified requirements by generating and evaluating all possible solution alternatives.

Zusammenfassung

Abstract—Sensors integration with existing industrial robotics control system is a challenge for researchers to develop online guiding algorithms for new applications where intelligent robot control is required. Different software platforms for sensors integration with industrial robot controller over Ethernet network have been created and studied in this research. The aim of this work is to provide alternative solution for sensor integration with the industrial robots controllers that have limited interface options in order to extend their performance and the applications where they can be used. By doing so, online motion control based on external sensors data for collision avoidance or online trajectory generator will be possible even with low cost industrial robots. Several tests were conducted on the communication between the platforms and the controller with the focus on data transmission time over different scenarios.

Zusammenfassung

Das Institut für Automatisierungstechnik und Softwaresysteme (IAS) gehört dem Fachbereich Elektrotechnik und Informationstechnik der Fakultät Informatik, Elektrotechnik und Informationstechnik der Universität Stuttgart an. Die Forschung und Lehre des Instituts konzentriert sich auf das Thema Softwaresysteme für die Automatisierungstechnik. Dabei verstehen wir uns als Brückenkopf der Produkt und Anlagenautomatisierung in die Forschungsdisziplinen der Informationstechnik, Softwaretechnologie und Automatisierung. Im Umfeld von Industrie 4.0 und Cyber-Physischen Systemen beschäftigt sich das IAS mit der Flexibilisierung, Bewertung und Simulation sowie IT-Architekturen und dem Engineering von Industrie-4.0-konformen Automatisierungssystemen.

Zusammenfassung

In this paper a framework for large scale coupled simulation raising from Maxwell’s equations is presented.

Zusammenfassung

Wheeled walkers (WWs) are used to improve mobility and for fall prevention in older persons, but not all users are satisfied with the usability of WWs. Intelligent WWs are being developed to improve the usability. The aim of this study was to support the development of intelligent WWs by investigating possible problems of using a WW. This study investigated 22 geriatric in-patients (median age 82 years) with and without their WW while opening a door against the direction of walking and passing through. Other possible problems when using WWs were identified by interview. Walking through the door was faster without than with using the WW (8.71 versus 12.86 s, p < 0.001), while interference between door and WW was documented in 41 of 44 (93 %) cases. Backward walking performance was better when using a WW with regard to gait speed, step width and walk ratio (all p < 0.002). Most referred problems when using a WW were walking downhill (83 %) and uphill (77 %) and obstacle crossing in general (77 %). Problems with opening a door against the direction of walking and the optimization of downhill and uphill walking as well as obstacle crossing should be regarded when developing an intelligent WW.

Zusammenfassung

Existing production systems need to become more flexible and reconfigurable in order for manufacturing companies to stay competitive in nowadays highly dynamic markets. To achieve this goal, existing machines have to undergo various kinds of adaptation processes. As currently there is no standardized procedure, adaptations are mostly individually performed which results in a time-consuming and error-prone process. A systematization of the adaptation process could guide engineers through this complex task and may reduce errors and execution time. Thus, this paper is aimed at proposing a systematization approach for the adaptation of manufacturing machines. The application of the approach is illustrated by an adaptation scenario of a testing machine.

Zusammenfassung

Interview: Professor Michael Weyrich, Institutsdirektor des Instituts für Automatisierungstechnik und Softwaresysteme (IAS), im Gespräch mit der atp edition.

Zusammenfassung

In industrial automation systems, when a problem occurs at run-time and no technician is available instantly, it can result in the stopping of the whole industrial automation system. Often, the occurred problem addresses only few system functions. Theoretically, other functions can be performed. However, in most of the cases, none of the system functions can be used. In order to prevent the stop of the whole system and guarantee the performance of still available functions, this paper proposes an intelligent multi-agent based problem management system. This system receives the diagnosis results from the industrial automation system by means of a local communication agent. Problems are discussed in two cases, i.e. known problems and unknown problems. For known problems, the intelligent multi-agent based problem management system directly accesses the database to get the available functions and sends this information to industrial automation systems. For unknown problems, it analyzes the effect of problems as well as the scope of them in order to identify available functions with the help of different agents. Thus, these information about available functions are stored into the database and are sent to industrial automation systems. Consequently, the industrial automation system can continue to work with the available functions. Hence, the intelligent multi-agent based problem management system can enhance the availability of industrial automation systems through ensuring available functions and preventing long system break-down.

Zusammenfassung

Steuerungssysteme für die Anlagenautomatisierung werden zunehmend dezentral realisiert. Zukunftsfähige Konzepte für solche Systeme müssen vor dem Hintergrund von Industrie 4.0 und dem Internet der Dinge betrachtet werden. Kürzlich veröffentlichte Referenzarchitekturen weisen den Weg für zukunftsorientierte Steuerungssysteme. Gleichzeitig stellt die Referenzarchitektur AUTOSAR eine etablierte Architektur für dezentrale Systeme in der Automobilindustrie dar, die vor ähnlichen Herausforderungen stand, wie die Automatisierungstechnik heute und somit als Vorbild dienen kann. In diesem Beitrag wird die AUTOSAR-Architektur betrachtet und teilweise mit anderen Architekturen verglichen. Auf dieser Basis wird ein Vorschlag zur Umsetzung auf Steuerungsebene entwickelt, um die Komplexität der Steuerungssoftware für dezentrale mikrocontrollerbasierte Systeme beherrschbar zu machen.

Zusammenfassung

At present most of fault diagnosis systems are dedicated to fault detection, fault elimination and fault effect analysis with specific diagnosis approaches. Even though fault effect based on these approaches concerns the affected components as well as functions. However, there is no analysis of the still available functions which could continue to operate despite the failure. Currently, there are few approaches working on the fault analysis based on the existing fault diagnosis system. To support a full fault diagnosis, this paper proposes a novel full fault effect analysis approach based on system models. Within a defect component in an automation system, available functions can be identified by the presented approach. The presented approach uses the results of the existing fault diagnosis system as an analysis basis, e.g. the fault ID and the fault location. The propagation of a fault is identified with the help of the requirement-function-component models, which are provided by the manufacturer. As a result, the presented approach is able to identify the available functions in case of the appearance of defect components. In this way, it enables an industrial automation system to continue its operation with restricted functionality despite a fault.

Zusammenfassung

This paper puts forward a novel concept of a Remote Problem Management System that allows an industrial automation system continuing to work with available functions during the appearance of a problem. The concept of the Remote Problem Management System consists of passive and active management to handle problems. The passive management aims at handling known problems by means of receiving the Fault-ID from a Fault-Diagnosis System, accessing the producers’ knowledge base and getting available functions. On the contrary, the active management attains to handle unknown problems via processing diagnosis results, analyzing the sphere of the influence, confirming available functions and storing these information in the producers’ knowledge base with the help of this system, the producer is able to manage the problems in his sold machines remotely and the user can use his machine with available functions before the machine is repaired.

Zusammenfassung

Due to increasing requirements on functionality (e.g. self-diagnosis, self-optimization) or flexibility (e.g. self-configuration), future automation systems are demanded to be more and more intelligent. Therefore the systems are desired to learn new knowledge from other systems or its environment. The purpose of this work is to propose a prospective concept of learning assistant for networked automation systems. With the help of the assistant, an automation system can obtain new knowledge by collaborating with other systems to improve its prior knowledge. So that the system user is liberated from continuously providing new knowledge to an automation system.

Zusammenfassung

Die Ansätze von Industrie 4.0 versprechen eine Veränderung der Produktionsorganisation und -technologie. Die darin geforderte IT-Vernetzung und Flexibilisierung der Produktion ändert den Wertschöpfungsprozess elementar. Dabei ergeben sich eine Reihe neuer Anforderungen zu dessen Absicherung. Besonders für den Produktionsstandort Deutschland spielt die Einhaltung von Qualitätsanforderungen eine zentrale Rolle. Um Ausschuss und Stillstandszeiten zu vermeiden, ist das systematische Testen der korrekten Funktionsfähigkeit von Produktionsanlagen vor Inbetriebnahme schon heute etabliert. Das Testen während des Engineerings und der Inbetriebnahme wird wegen der Dynamisierung der Produktion zukünftig nicht mehr ausreichen. Die Fähigkeit zur Absicherung der Produktion von morgen nimmt eine Schlüsselrolle ein, um die Wirtschaftlichkeit und somit die Akzeptanz für Industrie 4.0 zu steigern.

Zusammenfassung

Testen spielt in der Anlagenautomatisierung eine zentrale Rolle. Dabei stellt Industrie 4.0 durch die Flexibilisierung und Vernetzung eine Reihe neuer Anforderungen, um die Zuverlässigkeit von Automatisierungssystemen gewährleisten zu können. Geringe Ausfallzeiten sind eine Grundvoraussetzung, um die Akzeptanz bei Unternehmen für Industrie 4.0 zu steigern. Bisher fehlen Lösungen, wie die neuen Anforderungen gehandhabt werden können, um die vernetzte Produktion der Zukunft ausreichend abzusichern. Gleichzeitig zeichnet sich ab, dass durch die Komplexitätszunahme der Stellenwert des Testens weiterhin zunehmen wird.

Zusammenfassung

This paper provides a discussion on the coming technological changes in process automation of networked production systems, which will change the testing procedure. In the smart factory of the future there will be no possibility to reach a test coverage of 100%, assuming a flexible automation with continuous reconfiguration and dynamic changes during runtime. Consequently, large amounts of test cases and powerful algorithms for their prioritization are needed in order to certify the correct functionality of the production systems in the network. A concept is presented on how to analyze and prioritize the enormous amount of test cases resulting from the changes during runtime. The proposed approach for test case selection utilizes information of the product, the process and the status of the production machine for the prioritization and selection.

Zusammenfassung

Der Wandel von einer statischen Produktion zu einer dynamischen Produktion gemäß Industrie 4.0 verändert den Produktionsablauf elementar. Dieser disruptive Wandel der industriellen Automatisierungstechnik stellt enorme Ansprüche an die Sicherstellung der korrekten Funktionsweise von Produktionsnetzwerken. Innovative Testkonzepte und -methoden werden benötigt, welche den neuartigen, sich über den Lebenszyklus ändernden Anforderungen der flexiblen Produktionsanlagen gerecht werden. Die neuen Herausforderungen zur Sicherstellung der korrekten Funktionsweise der Produktion resultieren hauptsächlich aus fünf Annahmen, welche sich aus der Industrie-4.0-Strategie und den Kundenwünschen als „Wandlungstreiber“ ableiten lassen. Diese werden im Rahmen der Veröffentlichung aufgezeigt.

Zusammenfassung

This paper presents an approach for estimating contact positions of peg-in-hole assembling application using data models created from Force/Torque sensor inputs. The assembling task is carried by an industrial robot where the F/T sensor is mounted between the robot and the gripper to measure six values of forces and torques during the contact between the peg and the hole. The search approach to estimate the current position of the contact point is implemented based on human like intuitive behavior by maneuvering the peg on the hole’s edge. Different models were created and evaluated using experiments results acquired from pervious assembling attempts. Assembling steps, data analysis and models results are presented in this research.

Zusammenfassung

In diesem Beitrag wird die Notwendigkeit neuer Planungsverfahren für Cyber-physische Wertschöpfungsnetzwerke aufgezeigt sowie ein in fünf Schritte gegliedertes Planungskonzept vorgestellt. Dieses Planungskonzept skizziert ein Verfahren mit dem unterschiedliche Produktionsprozesse spezifiziert und auf einem virtuellen Marktplatz ausgewählt werden können. Das Konzept eines „eProduction-Systems“ wird am Beispiel einer verteilten Modellfabrik vorgestellt.

Zusammenfassung

Background: Wheeled walkers (WWs) are used to improve mobility and for fall prevention in older persons, but not all users are satisfied with the usability of WWs. Intelligent WWs are being developed to improve the usability. Aims: The aim of this study is to support the development of intelligent WWs by investigating possible problems of using a WW. Methods: This study investigated 22 geriatric in-patients (median age 82 years) with and without their WW while opening a door against the direction of walking and passing through. Other possible problems when using WWs were identified by interview. Results: Walking through the door was faster without than with using the WW (8.71 versus 12.86 s, p < 0.001), while interference between door and WW was documented in 41 of 44 (93 %) cases. Backward walking performance was better when using a WW with regard to gait speed, step width and walk ratio (all p < 0.002). Most referred problems when using a WW were walking downhill (83 %) and uphill (77 %) and obstacle crossing in general (77 %). Conclusions: Problems with opening a door against the direction of walking and the optimization of downhill and uphill walking as well as obstacle crossing should be regarded when developing an intelligent WW.

Zusammenfassung

This research presents and evaluates an approach to control and communicate with industrial robot control system over Ethernet network in order to implement online guiding and live data exchange. One of the main benefits of this fully-telematic control is to give the ability to integrate external sensors so that a guarded-motion can be implemented or to use sensor-guided motion by generating online trajectories. A comparison in terms of execution time, sensor integration, and security is made between the proposed fully-telematic control approach on one side, and the semi-telematic approach and the classical offline control of industrial robot on the other side.