Manuel Müller

Abstract

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.

Abstract

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.

Abstract

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.