Bearing fault diagnosis based on high-confidence pseudo-labels and dual-view multi-adversarial sparse joint attention network under variable working conditions-Reference-Cited by-同舟云学术

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Bearing fault diagnosis based on high-confidence pseudo-labels and dual-view multi-adversarial sparse joint attention network under variable working conditions

Published:2024-07 Issue: Volume:133 Page:108625
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Pan Cailu^ORCID,Shang Zhiwu^ORCID,Li Wanxiang^ORCID,Liu Fei^ORCID,Tang Lutai^ORCID

Publisher

Elsevier BV

Reference44 articles.

1. Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions;An;Expert Syst. Appl.,2023

2. Rolling bearing fault diagnosis method base on periodic sparse attention and LSTM;An;IEEE Sensor. J.,2022

3. Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery;Cao;IEEE Sensor. J.,2022

4. Dual-path mixed-domain residual threshold networks for bearing fault diagnosis;Chen;IEEE Trans. Ind. Electron.,2022

5. An adaptive activation transfer learning approach for fault diagnosis;Chen;IEEE ASME Trans. Mechatron.,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network;Mechanical Systems and Signal Processing;2025-01

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