Bearing fault diagnosis for variable working conditions via lightweight transformer and homogeneous generalized contrastive learning with inter-class repulsive discriminant

As indispensable components of rolling axle boxes, the condition of the bearings affects the safety of the traveling train. Therefore, bearing fault diagnosis is an imperative prerequisite for train safety. However, the diagnosis performance under variable working conditions is degraded owing to the large difference in the sample distribution and fewer samples. Although unsupervised domain adaptation models can solve these problems, environmental noise causes the fault features extracted from the two domains to overlap. Ultimately, the discriminative properties of the different samples remain insufficient. Therefore, we propose a rolling fault diagnosis approach for variable working conditions via lightweight Transformer and homogeneous generalized contrastive learning with inter-class repulsive discriminant (HGCL-ICRD). First, a deformable Transformer with lightweight manner is constructed to extract fault features from historical working conditions. Then, the source domain clustering cluster points are used to construct the positive and negative samples of the target domain to achieve the redistribution of the number. On this basis, the homogeneous generalized contrastive learning approach is built to make the samples to be tested have better classifiability. Finally, an inter-class repulsive discriminant term is constructed to minimize the sample distributional difference between the two domains. Furthermore, we construct an improved gray wolf algorithm to optimize the HGCL-ICRD. Extensive experiments on three datasets demonstrate that our model can perform high-precision and high-efficiency diagnosis under variable working conditions.