Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of
engineering equipment. Many intelligent diagnosis methods have been successfully …

Due to the extremely limited prior knowledge, machinery fault diagnosis under varying
working conditions with limited annotation data is a very challenging task in practical …

Intelligent fault diagnosis has been a promising way for condition-based maintenance.
However, the small sample problem has limited the application of intelligent fault diagnosis …

Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …

Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …

In the case of a compound fault diagnosis of rotating machinery, when two failures with
unequal severity occur in distinct parts of the system, the detection of a minor fault is a …

Generalizing deep models to unseen working conditions is an essential topic for intelligent
fault diagnosis. Existing domain generalization-based fault diagnosis (DGFD) methods …