Integrating Expert Trustworthiness Into Digital Twin Models Extracted from Expert Knowledge and Internet of Things Data: A Case Study in Reliability

The extraction of Digital Twin models from both expert knowledge and Internet of Things data remains an underexplored area, with existing approaches typically being highly customized. Expert knowledge, provided by human experts, is influenced by individual experience, contextual understanding and domainspecific knowledge, leading to varying levels of uncertainty and trustworthiness. In this paper, we address the identified research gap by extending our previous work and introducing a novel approach that models and integrates expert trustworthiness into the extraction of what we term data-knowledge fused Digital Twin models. Key features of the approach are: quantifications of expert trustworthiness and algorithms for selecting and integrating knowledge into model extractions based on trustworthiness. We demonstrate our approach for quantifying and incorporating trustworthiness levels in a reliability modeling case study.