A bearing fault diagnosis method combining improved inception V2 module and CBAM

The traditional deep learning bearing fault diagnosis method has a complex network, many training parameters, and weak model generalization. In response to the above problems, under the background of industrial big data, a bearing fault diagnosis method combining the improved Inception V2 module and the CBAM attention mechanism is proposed. The improved Inception V2 module further broadens the branch network structure by adding the average pooling layer, thereby improves network expression ability. The bearing vibration signal is converted into a time-frequency image through wavelet transform, which is used as the input of the convolutional neural network. The features of the input are adaptively extracted through the improved Inception V2 module, and the extracted features are organized across channels. Through CBAM attention mechanism, dual attention weights of channel and space are generated, enhancing the features with high correlation and suppressing the features with low correlation. The generated feature data is input to the global average pooling layer and the fault diagnosis result is outputted. Experimental results show that this method can establish a "shallow" convolutional neural network model, reduce model parameters, speed up model convergence, and achieve an accuracy of 99.75%. At the same time, under different loads and high noise conditions, the model has good generalization. It is more suitable for application in industrial big data. © 2022, Editorial Board of Journal of Vibration Engineering. All right reserved.