Cao Yu, Du Yongzhi, Le Likun, Li Xiaoxue, Gao Yanfang
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing, China.
School of Mechanical and Transportation Engineering, Ordos Institute of Applied Technology, Inner Mongolia, China.
PLoS One. 2025 Jul 11;20(7):e0327342. doi: 10.1371/journal.pone.0327342. eCollection 2025.
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.
本研究提出了FCRNet，即一种快速傅里叶卷积残差网络，专为复杂运行条件下的矿井通风轴承故障诊断而设计。通过将残差学习与快速傅里叶卷积（FFC）相结合，FCRNet采用双分支架构来有效捕获局部空间特征和全局频率模式。一个频谱变换（ST）模块通过整合局部傅里叶特征（LFF）、全局傅里叶特征（GFF）和局部时域特征（LF），实现了对多尺度空间和频率信息的统一处理，克服了传统卷积方法的局限性。在公开可用数据集和我们自建平台上的测试结果验证了所提出的方法在各种噪声水平下优于几种现有的故障诊断方法，为矿井通风的状态监测提供了有力支持。
