Chinese Journal of Ship Research, Volume. 20, Issue 2, 3(2025)

A review of deep learning-based few sample fault diagnosis method for rotating machinery

Ke WU1, Jun WU1, Qiming SHU1, Weiming SHEN2, and Wenbin SONG1
Author Affiliations
  • 1School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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    References(61)

    [5] [5] WU K, XU W, SHU Q M, et al. Unknownclass recognition adversarial wk f open set domain adaptation fault diagnosis of rotating machinery[J]. Journal of Intelligent Manufacturing, 2024: 1–19. doi: 10.1007S10845024023952.

    [30] [30] KOCH G, ZEMEL R, SALAKHUTDINOV R. Siamese neural wks f oneshot image recognition[C]Proceedings of the 32nd International Conference on Machine Learning. Lille, France, 2015: 130.

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    Ke WU, Jun WU, Qiming SHU, Weiming SHEN, Wenbin SONG. A review of deep learning-based few sample fault diagnosis method for rotating machinery[J]. Chinese Journal of Ship Research, 2025, 20(2): 3

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    Paper Information

    Category: Ship Intelligent O&M, and Fault Diagnosis

    Received: Sep. 9, 2024

    Accepted: Feb. 25, 2025

    Published Online: May. 15, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.04175

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