Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion

Su, Naiquan and Li, Xiao and Zhang, Qinghua and Huo, Zhiqiang (2019) Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion. Shock and Vibration, 2019 . ISSN 1070-9622

Full content URL: https://doi.org/10.1155/2019/1982317

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Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion
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Abstract

Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.

Keywords:Fault Diagnosis, System Reliability
Subjects:H Engineering > H100 General Engineering
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Engineering
ID Code:35212
Deposited On:11 Apr 2019 07:45

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