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Global receptive field graph attention network for unsupervised domain adaptation fault diagnosis in variable operating conditions

Author

Listed:
  • Meiling Cai

    (Hunan Normal University)

  • Sheng Chen

    (Hunan Normal University)

  • Jinping Liu

    (Hunan Normal University)

  • Yimei Yang

    (Hunan Normal University
    Hunan Normal University
    Huaihua University)

  • Lihui Cen

    (Central South University)

Abstract

While deep learning has advanced significantly in machinery diagnosis, models trained on source domain data struggle with real-world applications due to varying operating conditions in the target domain. To address this, we propose a novel solution, the Global Receptive Field-based Graph Attention Network (GRF-GAT), for the fault diagnosis of varying conditions by the scheme of unsupervised domain adaptation. Unlike existing methods, GRF-GAT models class labels, domain labels, and associations and distributions among samples within a unified deep network. GRF-GAT outperforms other migration methods, achieving the highest diagnostic accuracy in case studies on three benchmark datasets: CWRU bearing dataset, SQ bearing dataset, Jiangnan University bearing dataset, and a real industrial dataset: Axial Fans fault dataset. The visualization results show that the model effectively extracts domain-divisible and domain-invariant features, exhibiting research prospects and application potential. The code library is available at https://github.com/MrTree777/GRF-GAT .

Suggested Citation

  • Meiling Cai & Sheng Chen & Jinping Liu & Yimei Yang & Lihui Cen, 2025. "Global receptive field graph attention network for unsupervised domain adaptation fault diagnosis in variable operating conditions," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3285-3312, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02401-7
    DOI: 10.1007/s10845-024-02401-7
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    References listed on IDEAS

    as
    1. Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.
    2. Fuqiang Liu & Yandan Chen & Wenlong Deng & Mingliang Zhou, 2023. "Entropy-Optimized Fault Diagnosis Based on Unsupervised Domain Adaptation," Mathematics, MDPI, vol. 11(9), pages 1-18, April.
    3. Meiling Cai & Yaqin Shi & Jinping Liu & Jean Paul Niyoyita & Hadi Jahanshahi & Ayman A. Aly, 2023. "DRKPCA-VBGMM: fault monitoring via dynamically-recursive kernel principal component analysis with variational Bayesian Gaussian mixture model," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2625-2653, August.
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