Fault Diagnosis of Rotating Machinery With Limited Expert Interaction: A Multicriteria Active Learning Approach Based on Broad Learning System

Recently, research on the fault diagnosis of rotating machinery, especially for the compound or unknown cases, has drawn increasing attention. Some advanced learning-based approaches have achieved good fault diagnosis performance to some degree. However, in practical applications, how to utilize pri...

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Vydáno v:IEEE transactions on control systems technology Ročník 31; číslo 2; s. 953 - 960
Hlavní autoři: Liu, Zeyi, Zhang, Jingfei, He, Xiao, Zhang, Qinghua, Sun, Guoxi, Zhou, Donghua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Active learning
Annotations
Broad learning system (BLS)
Correlation
Costs
Fault diagnosis
Feature extraction
Learning
Machinery
multicriteria active learning (MCAL)
Multiple criterion
Rotating machinery
Task analysis
Vibrations
ISSN:1063-6536, 1558-0865
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Shrnutí:Recently, research on the fault diagnosis of rotating machinery, especially for the compound or unknown cases, has drawn increasing attention. Some advanced learning-based approaches have achieved good fault diagnosis performance to some degree. However, in practical applications, how to utilize prior knowledge as fully as possible for fault diagnosis with constraints of limited expert interaction remains an open issue. In this brief, a fault diagnosis methodology of rotating machinery with limited expert interaction is proposed. With related feature extraction techniques, a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation. In addition, the broad learning system (BLS) is exploited to achieve fast incrementally updating or retrain procedures with high diagnostic accuracy in different diagnosis scenarios. Several experiments are conducted on a real-world rotating machinery fault diagnosis (RMFD) experimental platform. Compared with other existing advanced approaches, the diagnosis performance of the proposal shows high stability and flexibility. The annotation cost of experts is also significantly reduced, which makes the proposal more suitable for dealing with practical problems.
Bibliografie:ObjectType-Article-1
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2022.3200214