Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

Zhang, Tianci; Chen, Jinglong; Li, Fudong; Zhang, Kaiyu; Lv, Haixin; He, Shuilong; Xu, Enyong

Zhang, Tianci; Chen, Jinglong; Li, Fudong; Zhang, Kaiyu; Lv, Haixin; He, Shuilong; Xu, Enyong.

Affiliation

Zhang T; State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
Chen J; State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: jlstrive2008@mail.xjtu.edu.cn.
Li F; State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
Zhang K; State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
Lv H; State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR 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.

ISA Trans ; 119: 152-171, 2022 Jan.

Article in En | MEDLINE | ID: mdl-33736889

Key words

Classifier design; Data augmentation; Feature learning; Intelligent fault diagnosis; Meta-learning; Small & imbalanced data; Zero-shot 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 Country of publication: United States

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