Fault Diagnosis Method of Rolling Bearing Combining Jump Connected Variational Auto-encoder with CNN
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.
国家科技期刊平台
NSTL
万方数据
维普
