A Fault Diagnosis Method for Centrifugal Compressors Based on Ontology and Bayesian Network Fusion Reasoning

ABSTRACT As a core industrial equipment, the stable operation of centrifugal compressors is crucial to production. Current research on its fault diagnosis mostly focuses on structured monitoring data, with insufficient mining of unstructured operation and maintenance experience data. To address this, this paper constructs an intelligent diagnosis model integrating ontology knowledge reasoning, knowledge graph modeling, and Bayesian Network (BN), realizing cross‐modal fault accurate localization and root cause analysis through the deep integration of “knowledge + probability.” Firstly, an ontology knowledge model is established based on fault information mined from unstructured data (such as fault reports and maintenance records), enabling standardized expression and semantic association of fault knowledge. The model is then imported into the Neo4j database, and specific fault information files are exported through Python queries to serve as the basic data for BN reasoning. Next, a probabilistic reasoning model between fault components and symptoms is built based on BN. Combining expert experience and historical data, the node conditional probabilities are determined to describe the uncertainty of fault propagation. Finally, a fusion method of ontology and BN is designed: ontology reasoning is used to optimize the BN structure, and intelligent diagnostic reasoning is realized through dynamic updating of posterior probabilities. Experiments using fault reports of centrifugal compressors from a certain enterprise show that the proposed fusion model can improve the interpretability and dynamic reasoning ability of fault diagnosis. Case verification demonstrates that the fault recognition accuracy of this method reaches 85%, indicating good performance. Therefore, this research provides a feasible solution for utilizing unstructured operation and maintenance data, enhancing the practicality of intelligent diagnosis for complex industrial equipment, which can shorten fault downtime, reduce maintenance costs, and thus has practical application value.