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

针对工业场景下滚动轴承信号易受噪声干扰,导致故障诊断准确率低和稳定性差的问题,本文提出一种基于软阈值降噪的脉冲卷积神经网络诊断方法。该方法使用软阈值滤波去噪,运用带时间标签的卷积层处理二维信号,增强动态特征提取能力。同时,通过引入IF和LIF神经元实现对时域和频域信息的联合编码,并采用替代梯度法进行端到端训练。实验结果显示,在信噪比为6dB时,所提方法的诊断准确率达100%,在信噪比为-6dB时诊断准确率达77.33%,优于其他常用方法,表明所提方法在噪声下具有良好的诊断效果和稳定性。

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.