The work aims to propose a knowledge graph-based fault diagnosis system design and reasoning method to address fragmented knowledge organization, complex relational coupling, and limited interpretability in complex equipment fault diagnosis. A diagnosis-oriented knowledge graph with five core entity types, namely equipment, fault, symptom, cause, and solution, was constructed. Graph traversal was used to retrieve candidate faults, and Bayesian confidence assessment was used to rank the candidates and explain the inferred paths. A three-layer architecture consisting of the presentation layer, logic layer and data layer was adopted. Typical test cases were used to verify diagnostic performance and scalability. Under the current closed-set testing conditions, the Top-1, Top-3, and Top-5 hit rates across 20 independent test cases were all 100.0% and the overall accuracy was 92.5%. The system remained excellent stability under conditions of knowledge graph scale expansion, increased reasoning depth, and concurrent access. This method can support knowledge organization, candidate ranking, and explainable diagnosis in complex equipment fault scenarios effectively.
Key words
knowledge graph /
complex equipment /
fault diagnosis /
graph traversal /
Bayesian inference /
explainable diagnosis /
knowledge service
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Funding
Equipment Quality Technology Basic Project of XX Department (2026-JSZBJCXX-F001)
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