Bearing fault diagnosis method based on soft threshold denoising for spiking convolutional neural network
LI Hao1, HUANG Xiaofeng1, ZOU Haojie2, SUN Yingjie1
1. College of Railway Transportation, Hu’nan University of Technology, Zhuzhou, Hu’nan 412007; 2. College of Computer Science, Hu’nan University of Technology, Zhuzhou, Hu’nan 412007
Abstract The signals of rolling bearings are easily interfered by noise in industrial environments, which reduces fault diagnosis accuracy and worsens stability. This paper proposes a diagnostic method based on soft threshold denoising for spiking convolutional neural network. Soft threshold filtering for noise reduction is proposed in this paper. This paper uses time-tagged convolutional layers to process two-dimensional signals to enhance dynamic feature extraction capabilities. IF and LIF neurons are introduced to jointly encode time domain and frequency domain information, and the surrogate gradient method is used for end-to-end training. The results show that the diagnostic accuracy reaches 100% under the signal-to-noise ratio of 6dB, and still reaches 77.33% under the signal-to-noise ratio of-6dB. The results of this method have certain advantages compared with commonly used methods, which verifies that the proposed method has better diagnostic results and higher stability under noise.
2024, Vol. 25 
