TU X Y, DENG Q, ZHANG Z X, et al. Small sample fault diagnosis method of gearbox based on frequency band attention network[J]. Chinese Journal of Ship Research, 2025, 20(X): 1–10 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04384
Citation: TU X Y, DENG Q, ZHANG Z X, et al. Small sample fault diagnosis method of gearbox based on frequency band attention network[J]. Chinese Journal of Ship Research, 2025, 20(X): 1–10 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04384

Small sample fault diagnosis method of gearbox based on frequency band attention network

More Information
  • Received Date: February 24, 2025
  • Revised Date: April 04, 2025
  • Official website online publication date: April 08, 2025
© 2025 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Objectives 

    Deep learning-based fault diagnosis methods generally require a large number of fault samples. To achieve accurate gearbox fault diagnosis under small-sample scenarios, a novel diagnosis method based on frequency band attention network is proposed.

    Methods 

    Initially, through the reconstruction-encoding layer, the vibration signals are transformed into sub-band encoded signals that are easy to classify; Subsequently, the intrinsic band attention layer is constructed to fully mine the representative time-frequency features from sub-band encoded signals; Finally, the multiple feature fusion module is used to integrate the time-frequency feature information for fault recognition under small-sample conditions.

    Results 

    The validation results on a gearbox fault simulation test bench show that the proposed method achieves a fault diagnosis accuracy of 99.85% under small-sample conditions, outperforming the comparison models.

    Conclusions 

    The research results can provide a reference for the fault diagnosis of gearbox under small sample conditions.

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