Fault Diagnosis of Rolling Bearing with VMD Visualization and Deep Learning
The fault detection signal of rolling bearing has the characteristics of non-linearity and unevenness,and the charac-teristic quantity is difficult to extract.Therefore,a method combining variation modal decomposition(VMD)signal visualization and deep learning neural network is proposed to diagnose bearing faults.Firstly,VMD method on the original vibration signal of the bearing is performed to filter out the signal noise.Secondly,Hilbert-Huang algorithm is used to eliminate the"under-enve-lope"problem of VMD.Thirdly,the one-dimensional time series signal is visualized,and the two-dimensional feature map based on the Gramian Angular Field(GAF)is extracted.Finally,the convolutional neural network(CNN)is used to diagnose visualized images.The CNN network includes 2 convolutional layers and 2 pooling layers.The kernel of the convolutional layer is(5×5),and the pooling layer kernel is(2×2).The depth of two convolutional layers are 20 and 32 respectively.10 kinds of vibra-tion signals collected are diagnosed.The number of samples in the training set is 3791,and the training accuracy is 96.5%.The number of the samples in test set is209,and the test accuracy is95.2%.Therefore,the effectiveness of this method is proved.
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