Unsupervised domain adaptive bearing fault diagnosis based on cross-attention
In response to the issue of cross-domain fault diagnosis where vibration data distribution varies and fault labeled samples are insufficient under variable working conditions,an unsupervised domain adaptive bearing fault diagnosis method based on cross-attention was proposed.Firstly,a cross-domain module is used to align locally similar samples across domains and extract fault data features from both the source and target domains.Secondly,a domain alignment strategy based on the Wasserstein distance is employed to align the global marginal feature distribution.Finally,for the unlabeled target condition samples,a pseudo-label generation scheme is proposed to further align fine-grained class information.Experimental results demonstrate that the proposed method has high diagnostic accuracy and is more advantageous under variable working conditions.
万方数据
维普
