Which statement is more correct? Considering uncertainty in expert knowledge for Digital Twin models

What is it about?

We propose a novel approach that considers and integrates the uncertainty inherent in human expert knowledge into the extraction processes of Digital Twin models from both expert knowledge and Internet of Things data. Experts possess unique experiences, contextual understandings and judgements. Thus, their expert knowledge can be highly divergent, complex, ambiguous, and even incorrect or incomplete. Consequently, not all expert knowledge statements should be equally weighted in the resulting simulation models. Our proposed approach models uncertainty contained in expert knowledge and integrates expert knowledge and their associated uncertainties into Digital Twin models. We demonstrate our approach on a case study in reliability assessment of manufacturing systems.

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Photo by ThisisEngineering on Unsplash

Photo by ThisisEngineering on Unsplash

Why is it important?

Integrating expert knowledge into data-driven Digital Twin models can enable better-informed Digital Twin models. However, the extraction of Digital Twin models from both expert knowledge and Internet of Things data is an open research area in literature. We address this research gap by proposing approaches to integrate expert knowledge and handle challenges that need to be mitigated. In this paper, we propose an approach to calculate uncertainty in expert knowledge statements and integrate the extracted uncertainty into Digital Twin models.