A multiscale convolution neural network for bearing fault diagnosis based on frequency division denoising under complex noise conditions

The condition of bearings has a significant impact on the healthy operation of mechanical equipment, which leads to a tremendous attention on fault diagnosis algorithms. However, due to the complex working environment and severe noise interference, training a robust bearing fault diagnosis model is considered to be a difficult task. To address this problem, a multiscale frequency division denoising network (MFDDN) model is proposed, where the frequency division denoising modules are presented to extract the detail fault features, and multiscale convolution neural network is employed to learn and enrich the overall fault features through two-scale convolution channels communication. The stacking convolution pooling layers are adopted to deepen the large-scale convolution channel and learn abundant global features. To remove the noise in the small-scale convolution channel, the frequency division denoising layers are constructed based on wavelet analysis to acquire the features of noise, where the input feature map is separated into high frequency and low-frequency features, and a sub-network based on attention mechanism is established for adaptive denoising. The superior features of MFDDN are the fusion of important fault features at each scale and custom learning of fine-grained features for the adaptive denoising, which improves the network feature extraction capability and noise robustness. This paper compares the performance of MFDDN with several common bearing fault diagnosis models on two benchmark bearing fault datasets. Extensive experiments show the state-of-the-art performance including robustness, generalization, and accuracy compared to the other methods under complex noise environment.