Rolling Bearing Fault Diagnosis Model Based on Attention Mechanism and Multi-Channel Processing

A neural network model based on convolutional attention mechanism (CBAM) multi-channel processing was studied for the fault diagnosis of rolling bearings under noisy actual working conditions in the attitude adjustment mechanism of a certain missile assembly and docking equipment. The model firstly carries out feature extraction through the first layer wide convolutional kernel, which enhances the robustness of the model and maintains the characteristics of the temporal sequence signal; the two channels adopt the improved first layer wide convolutional kernel deep convolutional network (IWDCNN) and the long-short-term memory neural network (LSTM), which combines the advantages of both of them in order to maximize the effect of the convolutional network in extracting the features; using the attention mechanism to adaptively assign weights to each of the features of the two channels, in order to enhance the effectiveness of the feature extraction. The attention mechanism is used to adaptively assign the weights to each feature of the two channels, which enhances the effective features while suppressing the influence of the invalid features on the prediction results of the model. Through comparative experiments, the results show that the model has higher classification accuracy and better training efficiency under noisy data.