Failure Diagnosis Method of Motor Rolling Bearing Based on Continuous Wavelet Transform and Attention Mechanism Stacking
The rolling bearing failure diagnosis model based on convolutional neural network cannot distinguish the features according to their importance during training and has poor failure category diagnosis accuracy.To address these shortcomings,a convolutional neural network based on attention mechanism stacking is built to highlight the important features in the feature pictures.Firstly,the one-di-mensional failure signal is intercepted by continuous wavelet transform according to a specific length and is then converted into a two-dimensional time-frequency map as the input of the convolutional neural network.After this,the depth-separable convolution is in-troduced in the attention mechanism to reduce the number of parameters while highlighting the features.Experiments are conducted to investigate the impact of the number of attention mechanism stacks in the Inception module and the number of Inception block stacks in the network structure on the performance of network failure diagnosis,and different Batch Size is set in the network structure with the best performance in diagnosis accuracy to further explore the optimal model.The experimental results show that the improved network structure achieves optimal performance when two attention mechanisms are stacked in the Inception module and one Inception block is stacked in the network with the diagnostic accuracy being 100％.
万方数据
维普
