A Tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery
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DOI: 10.1016/j.ress.2022.108968
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- Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Zuo, Lin & Xu, Fengjie & Zhang, Changhua & Xiahou, Tangfan & Liu, Yu, 2022. "A multi-layer spiking neural network-based approach to bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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Cited by:
- Chen, Pengfei & Zhao, Rongzhen & He, Tianjing & Wei, Kongyuan & Yuan, Jianhui, 2023. "A novel bearing fault diagnosis method based joint attention adversarial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
- Liu, Jianing & Cao, Hongrui & Luo, Yang, 2023. "An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
- Xia, Pengcheng & Huang, Yixiang & Tao, Zhiyu & Liu, Chengliang & Liu, Jie, 2023. "A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Chaleshtori, Amir Eshaghi & Aghaie, Abdollah, 2024. "A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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Keywords
Tensor representation; Subspace learning; Tensor alignment; Fault diagnosis; Domain adaptation; Transfer learning; Rolling bearings; Rotating machinery;All these keywords.
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