Research on bearing fault diagnosis method based on cjbm with semi-supervised and imbalanced data-Reference-Cited by-同舟云学术

Research on bearing fault diagnosis method based on cjbm with semi-supervised and imbalanced data

Published:2024-08-13 Issue: Volume: Page:
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Li Sai,Peng Yanfeng^ORCID,Bin Guangfu,Shen Yiping,Guo Yong,Li Baoqing,Jiang Yongzheng,Fan Chao

Funder

National Natural Science Foundation of China

Hunan Provincial Science and Technology Innovation Talent Project

Hunan Provincial Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11071-024-10073-4.pdf

Reference32 articles.

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3. Tama, B.A., Vania, M., Lee, S., Lim, S.: Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals. Artif. Intell. Rev. 56(5), 4667–4709 (2023)

4. An, Y., Zhang, K., Chai, Y., Liu, Q., Huang, X.: Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions. Expert Syst. Appl. 212, 118802 (2023)

5. Li, S., Peng, Y.F., Shen, Y.P., Zhao, S.B., Shao, H.D., Bin, G.F., Yang, X.K., Fan, C.: Rolling bearing fault diagnosis under data imbalance and variable speed based on adaptive clustering weighted oversampling. Reliab. Eng. Syst. Saf. 244, 109938 (2024)