Multi-channel Calibrated Transformer with Shifted Windows for few-shot fault diagnosis under sharp speed variation

In engineering practice, mechanical equipment is mainly operated under the working conditions of sharp speed variations, which results the data distribution domain shift. Furthermore, the domain shift and the lack of data in engineering practice render severe challenges for existing intelligent mechanical faults diagnosis technologies. To this end, this paper proposed a Multi-channel Calibrated Transformer with Shifted Windows (MCSwin-T) for computing self-attention in each non-overlapping window which models the relations between the sequences of split patches. Meanwhile, a new partitioning approach is designed by shifting the windows and alternately use the two different partitioning approach to establish the connections across windows. To extract low-level features of the signal and maintain the positional information, a plurality of convolution layers is applied before transformer block. A normalized method which is a multi-channel multiplication of the vector generated by each residual block is also developed to calibrate activation and increase the stability of the optimization. To evaluate the effectiveness, the proposed method is compared with multiple advanced transformer methods in two case studies under speed transient conditions. The experimental results indicate the superiority and higher accuracy of the proposed method under few-shot domain shift condition.