Intelligent Fault Diagnosis of Cyber Physical Systems using Knowledge Graphs

Machine maintenance poses significant challenges and costs in equipment manufacturing. A considerable portion of the budget is dedicated to provide training materials (documentations) for service engineers to diagnose failure causes, as well as to cover their salaries and provide spare parts. Additionally, breakdowns adversely impact machine capacity, preventing customers from utilizing their equipment during downtime. The main reason for all these challenges is the lack of efficiently utilizing training documentations. To address this and reduce costs while mitigating the negative implications of machine breakdowns, we propose a two-phase framework consisting of knowledge graph construction and diagnosis. In the first phase, an upper-level ontology based on the requirements for fault diagnosis of Cyber Physical Systems is developed, by drawing inspiration from the Industrial Domain Ontology (IDO) and the Industrial Ontology Foundry-Maintenance Reference Ontology (IOF-MRO). In the second phase, SPARQL queries are executed on the knowledge stored in GraphDB, providing valuable insights for diagnosing machine failures.