IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v252y2024ics0951832024004800.html
   My bibliography  Save this article

Semi-supervised source-free domain adaptation method via diffusive label propagation for rotating machinery fault diagnosis

Author

Listed:
  • Su, Zhiheng
  • Lian, Penglong
  • Shang, Penghui
  • Zhang, Jiyang
  • Xu, Hongbing
  • Zou, Jianxiao
  • Fan, Shicai

Abstract

Traditional domain adaptation methods address the performance degradation of deep learning models in diagnostic tasks under varying working conditions, assuming that source data is available. With the increasing demand for data privacy protection, access to source data has become restricted, leading to the rise of source-free domain adaptation methods. However, without any labels in the target domain, the adaptation process and performance of the model may be unstable and impractical for real-world scenarios. To address these issues, a semi-supervised source-free domain adaptation method via diffusive label propagation (SSFDA-DLP) is proposed in this paper. With only one labeled target sample provided for each class, SSFDA-DLP can diffuse the label information to the unlabeled target data through repeated iterations of pre-training with labeled target data and annotating new target data that are adjacent to the labeled ones. Considering that label propagation may incorrectly annotate some unlabeled samples, and to make full use of the unlabeled target data, feature and probability spaces consistency regularization is utilized to further improve the performance of the pre-trained model. The effectiveness and superiority of our method in source-free domain adaptation diagnostic tasks were evaluated on four datasets, including bearings and gears.

Suggested Citation

  • Su, Zhiheng & Lian, Penglong & Shang, Penghui & Zhang, Jiyang & Xu, Hongbing & Zou, Jianxiao & Fan, Shicai, 2024. "Semi-supervised source-free domain adaptation method via diffusive label propagation for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  • Handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024004800
    DOI: 10.1016/j.ress.2024.110408
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024004800
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110408?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Tian, Jilun & Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Zheng, Xiaorong & Nie, Jiahao & He, Zhiwei & Li, Ping & Dong, Zhekang & Gao, Mingyu, 2024. "A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
    6. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. 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).
    8. Xia, Jingyan & Huang, Ruyi & Chen, Zhuyun & He, Guolin & Li, Weihua, 2023. "A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    9. Haihua Qin & Jiafang Pan & Jian Li & Faguo Huang, 2024. "Improved Conditional Domain Adversarial Networks for Intelligent Transfer Fault Diagnosis," Mathematics, MDPI, vol. 12(3), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Li, Qikang & Tang, Baoping & Deng, Lei & Yang, Qichao & Zhu, Peng, 2024. "Adaptive centroid prototype-based domain adaptation for fault diagnosis of rotating machinery without source data," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. 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).
    4. Lin, Yanzhuo & Wang, Yu & Zhang, Mingquan & Zhao, Ming, 2025. "A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    5. Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
    6. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    7. Dai, Menghang & Liu, Zhiliang & Wang, Jinrui & Zuo, Mingjian, 2024. "Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    8. Yang, Miaorui & Zhang, Kun & Sheng, Zhipeng & Zhang, Xiangfeng & Xu, Yonggang, 2024. "The amplitude modulation bispectrum: A weak modulation features extracting method for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    9. Liu, Jie & He, Zihan & Miao, Yonghao, 2024. "Causality-based adversarial attacks for robust GNN modelling with application in fault detection," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    10. Kim, Sunghyun & Seo, Yun-Ho & Park, Junhong, 2024. "Transformer-based novel framework for remaining useful life prediction of lubricant in operational rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    11. Zheng, Xiaorong & Nie, Jiahao & He, Zhiwei & Li, Ping & Dong, Zhekang & Gao, Mingyu, 2024. "A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    12. Lu, Feiyu & Tong, Qingbin & Jiang, Xuedong & Feng, Ziwei & Liu, Ruifang & Xu, Jianjun & Huo, Jingyi, 2024. "DPICEN: Deep physical information consistency embedded network for bearing fault diagnosis under unknown domain," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    13. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    14. Tian, Jilun & Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Pang, Zhendong & Luan, Yingxin & Chen, Jiahong & Li, Teng, 2024. "ParInfoGPT: An LLM-based two-stage framework for reliability assessment of rotating machine under partial information," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    16. Huo, Xiaosen & Yin, Yuan & Jiao, Liudan & Zhang, Yu, 2024. "A data-driven and knowledge graph-based analysis of the risk hazard coupling mechanism in subway construction accidents," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    17. Chen, Xirui & Liu, Hui, 2025. "Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    18. Miao, Mengqi & Wang, Yun & Yu, Jianbo, 2024. "Temporal self-supervised domain adaptation network for machinery fault diagnosis under multiple non-ideal conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    19. Ma, Hongbo & Wei, Jiacheng & Zhang, Guowei & Kong, Xianguang & Du, Jingli, 2024. "Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    20. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024004800. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.