Few-shot fault diagnosis of rolling bearing via mutual centralized learning combining simple and parameter-free attention
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40430-024-05180-7.pdf
Reference42 articles.
1. Wang L, Zou T, Cai K et al (2024) Rolling bearing fault diagnosis method based on improved residual shrinkage network. J Braz Soc Mech Sci Eng 46:172
2. Qu X, Zhang Y, Yin L (2023) Fault diagnosis for rolling bearing of road heading machine via SVDS-ICNN. J Braz Soc Mech Sci Eng 45:439
3. Zhang ZQ, Zhou FN, Zhang CS et al (2023) A personalized federated learning-based fault diagnosis method for data suffering from network attacks. Appl Intell 53:22834–22849
4. Yu JB, Zhang BY (2020) Manifold regularized stacked autoencodersbased feature learning for fault detection in industrial processes. J Process Control 92:119–136
5. Gao SZ, Shi S, Zhang YM (2022) Rolling bearing compound fault diagnosis based on parameter optimization MCKD and convolutional neural network. IEEE Trans Instrum Meas 71:8
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