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Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis

Author

Listed:
  • Xiao Zhang

    (Soochow University
    Soochow University)

  • Weiguo Huang

    (Soochow University
    Soochow University)

  • Rui Wang

    (Soochow University
    Soochow University)

  • Jun Wang

    (Soochow University
    Soochow University)

  • Changqing Shen

    (Soochow University
    Soochow University)

Abstract

Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height recently. However, their satisfactory performance heavily relies on the availability of abundant labeled data, which poses a challenge given the scarcity of fault data in industrial scenarios. In this paper, a novel self-supervised method named dual prototypical contrastive network (DPCN) is proposed for cross-domain few-shot fault diagnosis. The proposed method contains two different prototypical contrastive learning stages. Specifically, in the first stage, intra-domain prototypical contrast guides the model to learn class-wise discriminative features by enhancing prototype-instance compactness within the same domain. Subsequently, in the second stage of cross-domain prototypical contrast, the model learns domain-invariant features by realizing cross-domain prototype-instance matching and proximity. Besides, mutual information maximization is applied to ensure the reliability of the predicted result. We undertake few-shot fault diagnosis experiments involving cross-load and cross-speed scenarios in two case studies. The extensive experimental results validate the superiority of the proposed method compared with the comparative methods.

Suggested Citation

  • Xiao Zhang & Weiguo Huang & Rui Wang & Jun Wang & Changqing Shen, 2025. "Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 475-490, January.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02237-7
    DOI: 10.1007/s10845-023-02237-7
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    References listed on IDEAS

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    1. Xin Zhang & Haifeng Wang & Bo Wu & Quan Zhou & Youmin Hu, 2023. "A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2449-2462, June.
    2. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    3. Cuixia Jiang & Hao Chen & Qifa Xu & Xiangxiang Wang, 2023. "Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1667-1681, April.
    4. Ruohui Hu & Min Zhang & Zaiyu Xiang & Jiliang Mo, 2023. "Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2225-2240, June.
    5. Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
    Full references (including those not matched with items on IDEAS)

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