Adaptive Meta Transfer Learning with Efficient Self-Attention for Few-Shot Bearing Fault Diagnosis

Author:

Zhao Jun,Tang TangORCID,Yu Ying,Wang Jingwei,Yang Tianyuan,Chen Ming,Wu Jie

Funder

National Key Research and Development Project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Reference45 articles.

1. Xu Y, Li Z, Wang S et al (2021) A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 169(6):108502

2. Zhang Y, Xing K, Bai R et al (2020) An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement 157(99):107667

3. Guo C, Li L, Hu Y, et al. (2020) A deep learning based fault diagnosis method with hyperparameter optimization by using parallel computing IEEE. Access 8:131248–131256

4. Tz A, Jc A, Fl A , et al. (2021) Intelligent fault diagnosis of machines with small & imbalanced data: a state-of-the-art review and possible extensions - ScienceDirect ISA Transactions

5. 葛轶洲,刘恒,王言,等.小样本困境下的深度学习图像识别综述[J]. 软件学报, 2022, 33(1):18.

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