Deep learning (DL) has attained remarkable achievements in diagnosing faults for rotary
machineries. Capitalizing on the formidable learning capacity of DL, it has the potential to …

Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …

Recent years have witnessed the successful development of domain adaptation methods to
tackle cross-domain fault diagnosis problems. However, these methods require the target …

Recent years have seen the rapid development and marvelous achievement of deep
learning-based fault diagnosis (FD) methods which assume that training data and testing …

Recently, unsupervised domain adaptation fault diagnosis (FD) techniques, which learn
transferable features by reducing distribution inconsistency of source and target domians …

Intelligent fault diagnosis models based on transfer learning achieve cross-domain fault
identification under small training samples. Existing cross-domain diagnosis models assume …

Existing researches about unsupervised cross-domain bearing fault diagnosis mostly
consider global alignment of feature distributions in various domains, and focus on relatively …

Recently, subdomain adaptation has gained extensive interest in addressing the problem of
wind turbine (WT) fault diagnosis. However, current methods mainly focus on the subdomain …

The issue of restricted target domain tags and constrained information offered by a single
source domain in the intelligent fault diagnosis may be successfully resolved by multi-source …

Increasing the generalizability of intelligent diagnostic models amidst data distribution shifts
is receiving growing attention. Nevertheless, current domain generalization methods …