基于SWT与改进卷积神经网络的轴承故障诊断OACSTPCD

In allusion to the issue of traditional bearing fault diagnosis relying on expert experience and poor time-frequency feature extraction,resulting in low efficiency and accuracy,a bearing fault diagnosis model(SICNN)based on synchronous squeezed wavelet transform(SWT)and improved convolutional neural network(CNN)is proposed.The one-dimensional non-stationary vibration signal is converted into a high-frequency two-dimensional time-frequency map through SWT,which is used as the input of the convolutional neural network.The SRM module is introduced to pool and fuse the extracted features,adjust the appropriate feature weights of the convolutional channel,and improve the network′s representation ability.The fault diagnosis results are output by means of the Softmax layer.In order to verify the performance of the proposed model,experiments were conducted by means of the Case Western Reserve University bearing dataset.The results show that the fault diagnosis accuracy of the model was 99.88%.Compared with other methods,it has good feasibility and convergence performance,and has high practical application value.