Fault Diagnosis of Rolling Bearing Based on Discrete Fourier Transform and Efficient Channel Attention

Aiming at the problem that the feature receptive field extracted by traditional convolutional neural network(CNN) in rolling bearing fault diagnosis is limited by the shape of convolution kernel,a network (DFT-ECANet)combining discrete Fourier transform(DFT)and efficient channel attention(ECA)is proposed. Firstly,convert the original vibration signal into the frequency domain through DFT,and transform it into the time domain through convolution in the frequency domain and make the signal has a global receptive field in the time domain through inverse discrete Fourier transform(IDFT);then,concatenate the signal with the convoluted data on the channel dimension,assign weight to each channel data through ECA,focusing on features with high diagnostic performance;finally,the deep features of the model are further extracted through several convolution-pooling pairs,and the fault diagnosis of rolling bearing is performed by linking the pooling layers and the fully connected layer. The experimental results show that DFT-ECANet has high diagnostic accuracy and good generalization performance on the original vibration datasets,and the diagnostic process of the model is visualized through T-SNE dimensionality reduction;it can still maintains high accuracy,robustness and anti-noise property under fierce noise interference. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.