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Self-supervised fusion of deep soft assignments for multi-view diagnosis of machine faults

Author

Listed:
  • Chuan Li

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

  • Yifan Wu

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

  • Manjun Xiong

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

  • Shuai Yang

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

  • Yun Bai

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

Abstract

Fault patterns are often unavailable for machine fault diagnosis without prior knowledge. This makes it challenging to diagnose the existence of machine faults and their types. To address this issue, a novel scheme of deep soft assignments fusion network (DSAFN) is proposed for the self-supervised multi-view diagnosis of machine faults. To enhance the robustness of the model and prevent overfitting, random noise is added to the collected signals. In each view, vibration features are extracted by a denoising autoencoder. Using the extracted deep features, a soft assignment fusion strategy is proposed to fully utilize both the public and complementary information of multiple views. Critical diagnosis missions, including novel fault detection and fault clustering, are accomplished through binary clustering and multi-class clustering of DSAFN, respectively. Two diagnostic experiments are conducted to validate the proposed method. The results indicate that the proposed method performs better than state-of-the-art peer methods in terms of diagnostic accuracy and noise robustness.

Suggested Citation

  • Chuan Li & Yifan Wu & Manjun Xiong & Shuai Yang & Yun Bai, 2025. "Self-supervised fusion of deep soft assignments for multi-view diagnosis of machine faults," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2493-2507, April.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02360-z
    DOI: 10.1007/s10845-024-02360-z
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    References listed on IDEAS

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    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Adrián Rodríguez Ramos & José M. Bernal de Lázaro & Alberto Prieto-Moreno & Antônio José Silva Neto & Orestes Llanes-Santiago, 2019. "An approach to robust fault diagnosis in mechanical systems using computational intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1601-1615, April.
    3. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    4. 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.
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