Details of Research Outputs

Intelligent bearing fault diagnosis based on deep learning is one of the hotspots in mechanical equipment monitoring applications. However, traditional deep learning-based methods have weak anti-noise ability and poor generalization performance in noise environment. This paper presents a new simple and effective deep attention mechanism network, namely, dual-path mixed domain residual threshold network (DP-MRTN), which aims to improve the accuracy of rolling bearing fault diagnosis in a noise environment. The DP-MRTN combines the channel attention mechanism, spatial attention mechanism and residual structure. The soft threshold function is used as the nonlinear transformation layer and dilated convolution is introduced to establish a dual-path neural network, so as to select the important features in the signal without resorting to any signal denoising algorithm. The performance of the DP-MRTN is validated against those state-of-the-art results on the real threephase asynchronous motor experiment platform in Zhejiang University of Technology. We have achieved 99.97% (0.09%) accuracy on Gaussian white noise, 99.87% (0.12%) accuracy on Laplacian noise and 99.98% (0.02%) accuracy on real noise. The results show that the proposed method can significantly improve the accuracy of fault diagnosis in noise environment compared with the traditional deep learning method.