Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network

Abstract

Due to often working in the environment of variable speeds and loads, it is an enormous challenge to achieve high-accuracy fault diagnosis (FD) of rolling bearings (RB) via existing approaches. In the article, a novel FD approach of RB, named IVTN-SA, is proposed by integrating subdomain adaptation (SA) and an improved vision transformer network (IVTN). To begin with, a local maximum mean discrepancy is introduced to replace the popular distribution alignment strategy of the same fault type in different domains based on adversarial learning mechanism and global maximum mean discrepancy. Then, the traditional vision transformer net is improved by employing a deformable convolution (DC) module to replace plain counterparts in existing CNN architectures and using a recurrent neural network to obtain the position encoding adaptively. The proposed method makes full use of the strong ability of SA in domain adaptation, the distinctive advantage of DC on feature extraction based on local information and the excellent performance of vision transformer in representing complicated relationships based on global information, thus realizing the fusion of local and global information and overcoming the distribution difference caused by working condition fluctuation. Two experimental cases have been conducted to verify its effectiveness in various working conditions, and the results demonstrate our proposed approach can achieve more excellent performance on diagnosis accuracy and model complexity compared with existing methods.