电气技术2024，Vol.25Issue(2)：12-20,9.
基于软阈值降噪的脉冲卷积神经网络轴承故障诊断方法
Bearing fault diagnosis method based on soft threshold denoising for spiking convolutional neural network
摘要
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.关键词
故障诊断/软阈值/脉冲神经网络(SNN)/替代梯度法Key words
fault diagnosis/soft threshold/spiking neural network(SNN)/surrogate gradient method引用本文复制引用
李浩,黄晓峰,邹豪杰,孙英杰..基于软阈值降噪的脉冲卷积神经网络轴承故障诊断方法[J].电气技术,2024,25(2):12-20,9.基金项目
湖南省自然科学基金(2022JJ50088、2023JJ50198) （2022JJ50088、2023JJ50198）
