Data-driven machinery fault diagnosis methods have been successfully developed in the
past decades. However, the cross-domain diagnostic problems have not been well …

In the past years, the practical cross-domain machinery fault diagnosis problems have been
attracting growing attention, where the training and testing data are collected from different …

Recently, domain adaptation techniques have achieved great attention in solving domain-
shift problems of mechanical fault diagnosis. However, existing methods mostly work under …

In the past years, deep learning-based machinery fault diagnosis methods have been
successfully developed, and the basic diagnostic problems have been well addressed …

Despite the recent success in data-driven machinery fault diagnosis, cross-domain
diagnostic tasks still remain challenging where the supervised training data and …

Data driven-based intelligent fault diagnosis methods, as a promising approach, have been
widely employed in the health management and maintenance decision of rotating …

To realize fault identification of unlabeled data and improve model generalization capability,
domain adaptation technology has been increasingly applied to intelligent fault diagnosis of …

Recently, various fault diagnosis methods based on domain adaptation (DA) have been
explored to solve the problem of discrepancy between the source and target domains …

In the recent years, data-driven machinery fault diagnostic methods have been successfully
developed, and the tasks where the training and testing data are from the same distribution …

Recently, domain adaptation has received extensive attention for solving intelligent fault
diagnosis problems. It aims to reduce the distribution discrepancy between the source …