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Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions
Wang, Sihan1; Wang, Dazhi1; Kong, Deshan1; Li, Wenhui1; Wang, Jiaxing1; Wang, Huanjie2,3
Source PublicationIET CONTROL THEORY AND APPLICATIONS
ISSN1751-8644
2022-06-28
Pages12
Corresponding AuthorWang, Dazhi(wdz_neu2021@163.com)
AbstractAs one of the most widely used rotating machinery components, whether the bearing can operate stably is related to the reliability of the equipment and the safety of the staff. Therefore, efficient and accurate intelligent fault diagnosis (IFD) technology is necessary for modern industrial equipment. Bearing fault diagnosis based on deep learning methods has made great progress in recent years. However, most methods rely heavily on massive data and the domain shift phenomenon caused by the high-level compound variable working conditions would greatly affect the performance of the model. To solve the data sparsity and domain shift problem simultaneously, an effective feature disentanglement and restitution (FDR) few-shot method is proposed for IFD under multiple scenes. First, the vibration signals are preprocessed and input into the metric-based neural network. The model is trained based on the meta-learning method to extract task-level general features to alleviate the data sparsity problem. Then, the FDR method extracted task-related features from the information discarded by the convolution kernel at different scales and fused them with the output features of the embedding module to reconstruct task-specific features and alleviate the phenomenon of domain shift. Finally, the relational module automatically extracts the nonlinear relations between features and classifies them. A number of high-level compound variable working condition tasks were constructed on two experimental platforms, and the fault diagnosis tests with small samples and multiple scenes were carried out. The results show that our proposed method has superior accuracy and transferability under compound variable working conditions.
DOI10.1049/cth2.12315
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[52077027] ; Department of Science and Technology of Liaoning province[2020020304-JH1/101]
Funding OrganizationNational Natural Science Foundation of China ; Department of Science and Technology of Liaoning province
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000817780500001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49162
Collection综合信息系统研究中心_工业智能技术与系统
Corresponding AuthorWang, Dazhi
Affiliation1.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wang, Sihan,Wang, Dazhi,Kong, Deshan,et al. Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions[J]. IET CONTROL THEORY AND APPLICATIONS,2022:12.
APA Wang, Sihan,Wang, Dazhi,Kong, Deshan,Li, Wenhui,Wang, Jiaxing,&Wang, Huanjie.(2022).Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions.IET CONTROL THEORY AND APPLICATIONS,12.
MLA Wang, Sihan,et al."Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions".IET CONTROL THEORY AND APPLICATIONS (2022):12.
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