Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Variable speed is one of the common working conditions of mechanical equipment, which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the squeeze-excitation attention mechanism is embedded in sparse filtering algorithm to guide model training. Then, unsupervised feature extraction is carried out on multi-scale inputs from the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.