Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

Feng, Yong; Chen, Jinglong; Zhang, Tianci; He, Shuilong; Xu, Enyong; Zhou, Zitong

Feng, Yong; Chen, Jinglong; Zhang, Tianci; He, Shuilong; Xu, Enyong; Zhou, Zitong.

Affiliation

Feng Y; State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Chen J; State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: jlstrive2008@mail.xjtu.edu.cn.
Zhang T; State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
He S; School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China. Electronic address: xiaofeilonghe@guet.edu.cn.
Xu E; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Dongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, China.
Zhou Z; State Key Laboratory for Manufacturing and Systems, Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

ISA Trans ; 120: 383-401, 2022 Jan.

Article in En | MEDLINE | ID: mdl-33762094

Key words

Attention; Fault diagnosis; Few-shot classification; Meta-learning; Semi-supervised learning

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: ISA Trans Year: 2022 Document type: Article Affiliation country: China Country of publication: United States

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