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Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery

Author

Listed:
  • Ke Wu

    (Huazhong University of Science and Technology)

  • Wei Xu

    (The Second Ship Design and Research Institute of Wuhan)

  • Qiming Shu

    (Huazhong University of Science and Technology)

  • Wenjun Zhang

    (The Second Ship Design and Research Institute of Wuhan)

  • Xiaolong Cui

    (The Second Ship Design and Research Institute of Wuhan)

  • Jun Wu

    (Huazhong University of Science and Technology)

Abstract

Transfer learning methods have received abundant attention and extensively utilized in cross-domain fault diagnosis, which suppose that the label sets in the source and target domains are coincident. However, the open set domain adaptation problem which include new fault modes in the target domain is not well solved. To address the problem, an unknown-class recognition adversarial network (UCRAN) is proposed for the cross-domain fault diagnosis. Specifically, a three-dimensional discriminator is designed to conduct domain-invariant learning on the source domain, target known domain and target unknown domain. Then, an entropy minimization is introduced to determine the decision boundaries. Finally, a posteriori inference method is developed to calculate the open set recognition weight, which are used to adaptively weigh the importance between known class and unknown class. The effectiveness and practicability of the proposed UCRAN is validated by a series of experiments. The experimental results show that compared to other existing methods, the proposed UCRAN realizes better diagnosis performance in different domain transfer task.

Suggested Citation

  • Ke Wu & Wei Xu & Qiming Shu & Wenjun Zhang & Xiaolong Cui & Jun Wu, 2025. "Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3031-3049, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02395-2
    DOI: 10.1007/s10845-024-02395-2
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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. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    3. Xiaoyin Nie & Gang Xie, 2021. "A novel normalized recurrent neural network for fault diagnosis with noisy labels," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1271-1288, June.
    Full references (including those not matched with items on IDEAS)

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