„Plug-and-Simulate“ im Internet der Dinge

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

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

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

IoT-Systeme bringen neue Herausforderungen, wie den dynamischen Eintritt von heterogenen Komponenten zur Laufzeit mit sich, welche auch bei der Simulation von IoT-Systemen beachtet werden müssen. Ein möglicher Ansatz diesen Herausforderungen zu begegnen ist der Einsatz einer agentenbasierten Co-Simulation, welche jede IoT-Komponente eines IoT-Systems individuell in einer eigenen Simulation ausführt und über die Interaktion zwischen den zugrundeliegenden Modellen diese zu einer Gesamtsimulation zusammenfügt. Diese Interaktion unterteilt sich in Nachrichtenaustausch und prozessorientierte Interaktion zwischen den Komponenten. Der Fokus dieses Beitrags liegt auf der Simulation der prozessorientierten Interaktion zwischen Komponenten in einer Co-Simulation.

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

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.