Industrial digital twins based on enterprise modeling: architecture, methodology, and engineering applications

Abstract

As AI technology increasingly permeates industrial applications, scenario management within the context of industrial intelligence presents a complex, multidisciplinary, and spatiotemporally coupled challenge. The integration characteristics of cyber-physical-social systems make Digital Twins (DT) a promising solution. However, constructing an effective DT model for such scenarios necessitates the incorporation of detailed industrial knowledge related to the corresponding data. Formal modeling serves as a unified cognitive approach and a robust foundation for system development. Hence, this paper focuses on the scenario management challenges within the industrial intelligence context and introduces the concept of using formal modeling languages as the foundation for DTs. We propose a model management architecture and a modeling methodology tailored for scenario-specific digital twins and provide a corresponding metamodel for the modeling approach. To validate the efficacy of our architecture and models, we analyze a multi-enterprise network collaboration scenario in remote maintenance of engineering machinery. This exploration not only demonstrates the validity of the proposed models and architecture but also offers a conceptual DT model for enhancing remote machinery maintenance.