Fusing Expert Knowledge and Internet of Things Data for Digital Twin Models: Addressing Uncertainty in Expert Statements

Extracting Digital Twin models by fusing expert knowledge with Internet of Things data remains a challenging and open research area. Existing literature offers very limited approaches for seamless and systematic extraction of Digital Twin models from these combined sources. In this paper, we address the research gap by proposing a novel approach that considers and integrates the uncertainty inherent in human expert knowledge into the extraction processes of Digital Twin models. Given that experts possess unique experiences, contextual understandings and judgements, their 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 contributions include a comprehensive literature review on the uncertainty in expert knowledge and the proposal of an approach to integrate this uncertainty in the extraction of Digital Twin models from fused expert knowledge and IoT data. We demonstrate our approach through a case study in reliability assessment.