Requirements on Data Processing for Simulation Models in connected Industrial Digital Twins

In the automation industry, concepts such as Digital Twins, representing virtual counterparts of physical assets, enhance efficiency, sustainability, and economic viability by enabling e.g., optimizations, advanced monitoring, and predictive maintenance. To achieve this, Digital Twins rely on models (including simulation models), which in turn depend on data describing the current state of the physical asset. As connectivity increases, the available data extends beyond the data of the sensors of the asset itself. It extends to data from the surrounding environment and even beyond. This introduces new challenges for Digital Twins and their models in terms of data integration. In previous research it was shown that current implementations are not adequately prepared for this scenario. Currently, no analysis in the literature addresses how interaction within a connected environment alters the requirements for Digital Twins. Therefore, this paper explicitly identifies the emerging challenges associated with extending the information acquisition scope of Digital Twins beyond the immediate physical asset. This is accomplished through a systematic analysis of potential scenarios that consider the inclusion of external data. Our analysis reveals several new requirements, such as the need for an extended description of the model’s purpose and the specific requirements for the data input. Digital Twins must possess enhanced capabilities to identify appropriate data sources within a dynamic production environment, as well as methods to manage multiple, potentially redundant data streams effectively. Through this analysis, we aim to highlight potential research directions for Digital Twins in the automation industry and advocate for the integration of interconnectivity into Digital Twin systems.