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Fault diagnosis method for rotating machinery based on knowledge graph

Published:2022-11-22 author:SHENG Lin, MA Bo, ZHANG Yang. Browse: 476 Check PDF documents

Fault diagnosis method for rotating machinery based on knowledge graph

SHENG Lin1, MA Bo1,2, ZHANG Yang1

(1.College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing

100029, China; 2.Beijing Key Laboratory of High End Mechanical Equipment Health Monitoring and Self

Recovery, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract: In the fault diagnosis of rotating machinery, when the input symptom conditions were missing, the traditional rule-based fault diagnosis reasoning method would have a large deviation in the diagnosis results. Aiming at the problem,a rotating machinery fault diagnosis method based on knowledge graph was proposed. Firstly, an ontology-based knowledge representation method for rotating machinery fault diagnosis was described, and knowledge representation was carried out by constructing an ontology representation model for rotating machinery fault diagnosis knowledge, on the basis of which a knowledge graph for rotating machinery fault diagnosis was constructed. Then, combined with the pathbased knowledge graph inference method, the diagnosis method based on knowledge graph of rotating machinery fault diagnosis was proposed, and the reasons of fault were inferred by using the relationship between structures of rotating machinery equipment. Finally, taking the main pump of nuclear power plant as an example, the knowledge map of main pump fault diagnosis was constructed, and the rotating machinery fault diagnosis method based on knowledge map was verified. The results of experimental validation show that the diagnostic accuracy of the method reaches 92.1% under the condition of missing symptoms. It is far better than the accuracy of the traditional rule-based fault diagnosis reasoning method, and effectively solves the problem of low diagnostic accuracy when the symptoms are missing. At the same time, the application of knowledge graph can also provide a new idea for other intelligent diagnosis methods of mechanical equipment.

Key words: knowledge graph construction; input symptom missing; ontology-based knowledge representation method; analytical models; datadriven; rule-based fault diagnosis method

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