机械科学与技术2024，Vol.43Issue(4)：681-689,9.DOI:10.13433/j.cnki.1003-8728.20220273
跳连接变分自编码器与CNN相结合的滚动轴承故障诊断方法
Fault Diagnosis Method of Rolling Bearing Combining Jump Connected Variational Auto-encoder with CNN
摘要
Abstract
For the problem that the failure rate of rolling bearings is small and it is not easy to collect fault data,a novel rolling bearing fault diagnosis method with small samples is proposed,which combines respective advantages of jumping connection variational auto-encoder and deep convolution neural network with wide kernel.The proposed method firstly introduces a jump connection structure between encoding and decoding of the variational auto-encoder,and Tanh is used as the activation function of the network,thus improving the feature diversity of the generated samples.Secondly,the diagnosis model of wide kernel deep convolution network is constructed,aiming to enhance the capability of fault feature extraction from vibration signals.Finally,the data set expanded by the generated samples is used as the model input to improve the amount of feature information contained in the training set,thereby realizing bearing fault diagnosis under small samples.Experimental analysis shows that the proposed method can generate effective fake samples and gains high diagnostic accuracy in the case of small samples.关键词
故障诊断/跳跃连接变分自编码器/数据生成/宽核深度卷积神经网络Key words
fault diagnosis/jump connected variational auto-encoder/data generation/deep convolution neural network with wide kernel分类
机械制造引用本文复制引用
张洪亮,余其源,王锐..跳连接变分自编码器与CNN相结合的滚动轴承故障诊断方法[J].机械科学与技术,2024,43(4):681-689,9.基金项目
安徽省自然科学基金项目(2108085MG236)与安徽省普通高校重点实验室开放基金重点项目(CS2021-ZD01) （2108085MG236）
