Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions

doi:10.1049/cth2.12315

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	Few-shot multiscene fault diagnosis of rolling bearing under compound variable working conditions
	Wang, Sihan 1; Wang, Dazhi 1; Kong, Deshan 1; Li, Wenhui 1; Wang, Jiaxing 1; Wang, Huanjie2,3
发表期刊	IET CONTROL THEORY AND APPLICATIONS
ISSN	1751-8644
	2022-06-28
页码	12
通讯作者	Wang, Dazhi(wdz_neu2021@163.com)
摘要	As 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.
DOI	10.1049/cth2.12315
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[52077027] ; Department of Science and Technology of Liaoning province[2020020304-JH1/101]
项目资助者	National Natural Science Foundation of China ; Department of Science and Technology of Liaoning province
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000817780500001
出版者	WILEY
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49162
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Wang, Dazhi
作者单位	1.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
推荐引用方式 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.