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FCRNet: Fast Fourier convolutional residual network for ventilator bearing fault diagnosis

Author

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  • Yu Cao
  • Yongzhi Du
  • Likun Le
  • Xiaoxue Li
  • Yanfang Gao

Abstract

This study presents FCRNet, a Fast Fourier Convolution Residual Network, tailored for fault diagnosis of mine ventilation bearings under complex operating conditions. By integrating residual learning with Fast Fourier Convolution (FFC), FCRNet employs a dual-branch architecture to effectively capture local spatial features and global frequency patterns. A Spectral Transformation (ST) module achieves unified processing of multi-scale spatial and frequency information by integrating local Fourier features (LFF), global fourier features (GFF), and local time-domain features (LF), overcoming the limitations of conventional convolutional approaches. The testing results on publicly available datasets and our self-built platform validate that the proposed method outperforms several existing fault diagnosis methods at various noise levels, providing strong support for the condition monitoring of mine ventilation.

Suggested Citation

  • Yu Cao & Yongzhi Du & Likun Le & Xiaoxue Li & Yanfang Gao, 2025. "FCRNet: Fast Fourier convolutional residual network for ventilator bearing fault diagnosis," PLOS ONE, Public Library of Science, vol. 20(7), pages 1-22, July.
    • Handle: RePEc:plo:pone00:0327342
    DOI: 10.1371/journal.pone.0327342
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    References listed on IDEAS

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    1. Yonggang Gou & Xiuzhi Shi & Jian Zhou & Xianyang Qiu & Xin Chen, 2017. "Characterization and Effects of the Shock Losses in a Parallel Fan Station in the Underground Mine," Energies, MDPI, vol. 10(6), pages 1-20, June.
    2. Chatterjee, Arnab & Zhang, Lijun & Xia, Xiaohua, 2015. "Optimization of mine ventilation fan speeds according to ventilation on demand and time of use tariff," Applied Energy, Elsevier, vol. 146(C), pages 65-73.
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