Fault diagnosis of rolling bearing based on dual-dimensional attention residual shrinkage network
Active operation and maintenance technology is a necessary guarantee for mechanical equipment to improve its maintenance efficiency,reduce its maintenance cost,and ensure its long-term stable operation.In this paper,an intelligent fault diagnosis method based on dual dimensional attention residual shrinkage network is proposed,tak-ing rolling bearing as the research object.In the proposed method,firstly,a convolution attention module is intro-duced to weight the processed signal in its spatial dimension and channel dimension to strengthen important fea-tures,focusing the network's attention on information which is more critical for fault classification.Secondly,the functions of data denoising and removing redundant information in features are realized through an adopting soft thresholding module based on mixed-domain attention.Experimental results show that the proposed method can real-ize the effective diagnosis and classification of rolling bearing faults,and has good robustness.
rolling bearingdual-dimensional feature extractionsoft thresholdSwish activation functiondeep learning
万方数据
维普
