Knowledge Graph-Based Digital Twins for Automatic Bayesian Networks Generation

Much scientific literature claims that existing digital twins often lack semantics, leaving the plant maintenance team responsible for interpreting and responding to faults. To enable semantics in a digital twin, it must rely not only on the data produced by sensors, but also on a deeper knowledge of the system and the processes taking place within it. This paper proposes a framework for the automated generation of Bayesian Networks (BNs) from knowledge graphs, which should store information from different sources, such as topology, documents orig-inally written in natural language, and domain-specific ontologies based on RDF (Resource Description Framework). BNs will be used to infer failure symptoms and causes, while automated refinement of BNs is expected to address scalability issues. As a first representative demonstrator, a two-room facility was modeled in the Dymola environment and coded according to the Brick Ontology and the Digital Building Ontology. A BN was extracted from it and tested for fault analysis. Finally, the two knowledge graphs were compared to conclude on their efficiency for automated BN generation.