Class-Consistent Matching Attention Wavelet Networks for Partial Transfer Intelligent Diagnosis

In the case of label space alignment, the existing domain adaptation (DA)-based fault diagnosis approaches have achieved high accuracy. In real industrial scenarios, however, the label space of the target domain is usually a subset of the label space of the source domain, called partial DA (PDA). The main challenge of PDA lies in how to separate common samples from private samples. In existing works, different weights are usually assigned to different samples based on the prediction score of the classifier, but the negative transfer caused by the data distribution alignment of private and common samples is ignored. To address this problem, class-consistency matching is proposed in this article, which uses label consensus score to identify classes in target clusters to discover common and private samples. In addition, parameter-free cosine attention wavelet blocks (PCAWBs) are designed to learn the complementary spatial-domain and frequency-domain features to enrich the domain-invariant features extracted by the shared encoder. Experiments on the real motor system demonstrate that the proposed method significantly outperforms state-of-the-art PDA fault diagnosis approaches.