A Bearing Fault Diagnosis Model Based on Deformable Atrous Convolution and Squeeze-and-Excitation Aggregation

Abstract

Fault diagnosis has a direct impact on the economic benefits of the modern rotating machinery industry. The input of current fault diagnosis methods is mostly the original vibration signal or the time–frequency graph (TFG) obtained by short-time Fourier transform. Both of these inputs use many data points, which reduce the real-time performance of the fault diagnosis model. Therefore, a novel fault diagnosis model is proposed to achieve high accuracy when the input data points are few. First, the two inputs of the model are the frequency-domain signal (FDS) and TFG obtained by processing the original signal with fast Fourier transform and continuous wavelet transform. Then, convolution (Conv) and deformable atrous Conv are used to extract the FDS and TFG features, respectively. These features are then fused by squeezing-and-excitation aggregation. Finally, the outputs of three different dimensions are obtained. Experimental results show that the proposed feature extraction and fusion module can increase the generality of the diagnostic model, and the proposed model has a better effect on three outputs compared to state-of-the-art methods.