A modular fault diagnosis method for rolling bearing based on mask kernel and multi-head self-attention mechanism

Data-driven methods have been applied in fault diagnosis. However, in practical engineering, workers are more concerned with the real-time health status of bearings. And it is difficult to complete the effective training of diagnostic models with insufficient labeled fault data. Therefore, this paper proposes a modular method based on a mask kernel and multi-head self-attention mechanism for rolling bearing fault diagnosis. First, the proposed method divides the diagnosis into two modules of status detection and fault recognition. The approach of sharing one backbone for both modules simplifies the optimization process. The method combines the translation invariance of the convolution kernel and the mask attention mechanism of the transformer by computing the local self-attention and superimposing the partial local attention by the mask to ensure the integrity of the information. Finally, a zero-shot training method is proposed to embed the query into the model to achieve cross-distribution fault diagnosis of bearings. The experiments on the data sets of Case Western Reserve University and machinery fault simulator are implemented to diagnose the bearings. The results show that the proposed method can obtain higher diagnostic accuracy and computational efficiency than the existing methods and can be valid for scenarios with cross-condition diagnosis or imbalanced samples.