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A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions
Wang Y(王煜)1,2,3,4; Gao J(高洁)1,2,4; Wang W(王伟)1,2,4; Du JS(杜劲松)1,2,4; Yang X(杨旭)1,2,4
Department智能检测与装备研究室
Source PublicationMEASUREMENT SCIENCE AND TECHNOLOGY
ISSN0957-0233
2022
Volume33Issue:1Pages:1-17
Indexed BySCI ; EI
EI Accession number20214611143379
WOS IDWOS:000711180700001
Contribution Rank1
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDC04000000] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [62073312] ; Natural Science Foundation of Liaoning ProvinceNatural Science Foundation of Liaoning Province [2019-MS-343, 20180520016, 20180520008] ; LiaoNing Revitalization Talents Program ; K C Wong Education Foundation
Keyworddomain adaptation bearing fault diagnosis adversarial network attention mechanism
Abstract

In recent years, transfer learning technology has developed rapidly and has been widely used in bearing fault diagnosis. Most existing methods mainly align the overall feature distribution of the signal samples across the source and target domains. However, the transferability of each signal and each segment of a signal sample is different. Therefore, in this paper, a novel attention-based domain adaptation model (ADA) is proposed. The ADA model consists of a feature extractor and an ADA module. The feature extractor is built by separable convolution with channel attention module and length attention module to improve the reliability of feature learning. The ADA module consists of two parts, the local ADA module and the global ADA module to enhance the model's domain adaptation ability by focusing on the signals and signal segments with better transferability. The experimental results show that the ADA model is superior to other intelligent fault diagnosis methods based on transfer learning under variable working conditions.

Language英语
WOS SubjectEngineering, Multidisciplinary ; Instruments & Instrumentation
WOS KeywordNEURAL-NETWORK ; DEEP
WOS Research AreaEngineering ; Instruments & Instrumentation
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDC04000000] ; National Natural Science Foundation of China[62073312] ; Natural Science Foundation of Liaoning Province[2019-MS-343] ; Natural Science Foundation of Liaoning Province[20180520016] ; Natural Science Foundation of Liaoning Province[20180520008] ; LiaoNing Revitalization Talents Program ; K C Wong Education Foundation
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29851
Collection智能检测与装备研究室
Corresponding AuthorGao J(高洁)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province, Shenyang 110179, China
Recommended Citation
GB/T 7714
Wang Y,Gao J,Wang W,et al. A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions[J]. MEASUREMENT SCIENCE AND TECHNOLOGY,2022,33(1):1-17.
APA Wang Y,Gao J,Wang W,Du JS,&Yang X.(2022).A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions.MEASUREMENT SCIENCE AND TECHNOLOGY,33(1),1-17.
MLA Wang Y,et al."A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions".MEASUREMENT SCIENCE AND TECHNOLOGY 33.1(2022):1-17.
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