A new indirect transfer fault diagnosis method based on feature separation

Abstract

The existing fault diagnosis methods based on deep transfer learning achieve domain adaptation by matching shared features between the source and target domains. However, there are different private characteristics between the source domain and the target domain, and traditional cross-domain fault diagnosis algorithms ignore the impact of private attributes in the target domain on classification tasks. Meanwhile, the existence of private features causes a negative transfer problem for domain adaptation methods in domains with large aligned domain gaps. In practical applications, indirect transfer between the source domain and target domain can reduce the limitations of inherent attributes and enhance the guidance of private features in the target domain for classification tasks, which can enrich the transfer of fault knowledge and improve the effectiveness of cross-domain fault diagnosis. Therefore, an indirect transfer fault diagnosis method based on feature separation (ITFS) is proposed. Firstly, an attention-enhanced feature separation framework is proposed, which constructs a set of joint attention convolutional autoencoders (CAEs) to separate features from two domains, avoiding the influence of domain shift in private features. Secondly, to better adapt the two domains, an indirect domain adaptation method based on Gaussian distribution is proposed, which combines Gaussian indirect transfer learning with pseudo-labeling enhancement to reduce the domain gap in the two domains. Six transfer tasks on the PU datasets demonstrate that compared to MCD, the average accuracy of the proposed method is improved by 14.09 %, and it is superior to other popular existing methods. Experiments on three publicly available datasets have shown that this method outperforms other popular existing methods.