Fusing Expert Knowledge and Data for Simulation Model Discovery in Digital Twins: A Case Study from Reliability Modeling

Integrating expert knowledge in data-driven Digital Twins can lead to better-informed underlying models. Achieving systematic integration, however, remains a complex challenge. In this study, we propose an initial approach for hybrid model extraction by systematically fusing expert knowledge statements with Internet of Things data from manufacturing systems, such as event and state logs. We outline two main strategies to facilitate the fusion of data and expert knowledge in a systematic way. We, furthermore, present a case study in reliability assessment of manufacturing systems showcasing our methodology within this specific domain. Using our four fusion algorithms, we automatically extract reliability models from both data and expert knowledge statements. Finally, we conduct a comprehensive analysis of the results and draw conclusions regarding the efficacy of the fusion algorithms for Digital Twin model extractions.