A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis
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DOI: 10.1016/j.ress.2023.109892
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- Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
- Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
- Zhao, Ke & Hu, Junchen & Shao, Haidong & Hu, Jiabei, 2023. "Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Bian, Wenbin, 2023. "Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Li, Qikang & Tang, Baoping & Deng, Lei & Zhu, Peng, 2023. "Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
- Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
- Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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Cited by:
- Zhou, Tao & Yao, Dechen & Yang, Jianwei & Meng, Chang & Li, Ankang & Li, Xi, 2024. "DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
- Liu, Mengyu & Cheng, Zhe & Yang, Yu & Hu, Niaoqing & Yang, Yi, 2024. "Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
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Keywords
Rotating machinery; Fault diagnosis; Multi-source domain adaptation; Fine-grained feature decoupling;All these keywords.
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