Author:
Yu Mingyue,Yang Chunxue,Liu Liqiu,Su Jingwen
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials,Safety, Risk, Reliability and Quality,General Materials Science
Reference32 articles.
1. K.W. Tian, S.J. Dong, B.J. Jiang et al., A bearing fault diagnosis method based on an improved depth residual network. J. Vib. Shock. 40(20), 247–254 (2021)
2. S.Z. Gao, L.T. Xu, Y.M. Zhang et al., Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN. ISA Trans. 128, 485–502 (2022)
3. F. Dalvand, S. Dalvand, F. Sharafi et al., Current noise cancellation for bearing fault diagnosis using time shifting. IEEE Trans. Ind. Electron. 64(10), 8138–8147 (2017)
4. L.J. Wan, Y.Y. Li, K.Y. Chen et al., A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis. Measurement. 191, 110752 (2022)
5. Y.G. Zhou, S.T. Yan, Y.B. Ren et al., Rolling bearing fault diagnosis using transient-extracting transform and linear discriminant analysis. Measurement. 178, 109298 (2021)
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