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Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information

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  • Yu, Aobo
  • Cai, Bolin
  • Wu, Qiujie
  • García, Miguel Martínez
  • Li, Jing
  • Chen, Xiangcheng

Abstract

Fault diagnosis is crucial for reliability assessment of rotation machinery. Due to issues such as data privacy, it is impossible to get complete information for fault diagnosis in practical and challenging scenario. To solve aforementioned problem, fault diagnosis under partial information is studied. A source-free domain adaptation method for fault diagnosis, enabling cross-domain fault diagnosis without accessing the source data, is proposed. Firstly, multireceptive field graph convolutional(MRF-GCN) was used to aggregate different numbers of node information from different receptive fields for extracting more representative features. Secondly during the training process on the target domain, positive and negative pairs are constructed based on the samples’ neighbors and extended neighbors. Clustering and domain adaptation are then performed using a contrastive loss. Finally, information maximization loss is employed to improve the diagnostic accuracy. Experimental results demonstrate that, the proposed approach achieves favorable diagnostic performance under partial information, even without access to source domain data.

Suggested Citation

  • Yu, Aobo & Cai, Bolin & Wu, Qiujie & García, Miguel Martínez & Li, Jing & Chen, Xiangcheng, 2024. "Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002552
    DOI: 10.1016/j.ress.2024.110181
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    References listed on IDEAS

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    1. Lu, Biliang & Zhang, Yingjie & Liu, Zhaohua & Wei, Hualiang & Sun, Qingshuai, 2023. "A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    2. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. 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).
    4. Shi, Mingkuan & Ding, Chuancang & Wang, Rui & Shen, Changqing & Huang, Weiguo & Zhu, Zhongkui, 2023. "Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    5. Rahul Rai & Manoj Kumar Tiwari & Dmitry Ivanov & Alexandre Dolgui, 2021. "Machine learning in manufacturing and industry 4.0 applications," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4773-4778, August.
    6. 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).
    7. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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