A Novel Model Adaption Approach for intelligent Digital Twins of Modular Production Systems

Abstract

Industrial automation is becoming increasingly networked, intelligent and autonomous. Digital Twins, which serve as virtual representations, are a key technology in this context. The Digital Twin of a modular production system contains many different models that are mostly created for specific applications and fulfil different requirements. In particular, simulation models created in the development phase can be used in the operational phase for applications such as prediction or operation-parallel simulation. Due to the high heterogeneity of the model landscape in the context of a modular production system, the plant operator is faced with the challenge of adapting the models in order to ensure an application-oriented realism in the event of changes to the asset and its environment or the addition of applications. Therefore, this paper proposes an approach for the continuous model adaption in the Digital Twin of a modular production system during the operational phase. An agent-based implementation of the concept demonstrates the benefits of the approach for an operational phase application scenario.