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

• Published in: PloS one Vol. 20; no. 7; p. e0327342
• Main Authors: Cao, Yu, Du, Yongzhi, Le, Likun, Li, Xiaoxue, Gao, Yanfang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.07.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bearings; Bearings (Machinery); Condition monitoring; Deep learning; Equipment and supplies; Fault diagnosis; Fault location (Engineering); Fourier Analysis; Fourier transforms; Frequency dependence; Humans; Information processing; Inspection; Methods; Mine ventilation; Mining; Mining accidents & safety; Neural networks; Neural Networks, Computer; Noise levels; Propagation; Signal processing; Ventilation; Ventilators; Ventilators, Mechanical; Wavelet transforms; Working conditions; China

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 captur...