A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions
文献类型:期刊论文
作者 | 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 |
刊名 | MEASUREMENT SCIENCE AND TECHNOLOGY |
出版日期 | 2022 |
卷号 | 33期号:1页码:1-17 |
ISSN号 | 0957-0233 |
关键词 | domain adaptation bearing fault diagnosis adversarial network attention mechanism |
产权排序 | 1 |
英文摘要 | 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. |
WOS关键词 | NEURAL-NETWORK ; DEEP |
资助项目 | Strategic 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 |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
语种 | 英语 |
WOS记录号 | WOS:000711180700001 |
资助机构 | Strategic 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 |
源URL | [http://ir.sia.cn/handle/173321/29851] |
专题 | 沈阳自动化研究所_智能检测与装备研究室 |
通讯作者 | Gao J(高洁) |
作者单位 | 1.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 |
推荐引用方式 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. |
入库方式: OAI收割
来源:沈阳自动化研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。