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 traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Remaining useful life (RUL) prediction is the core research task of aero-engine prognostics
health management (PHM), which is crucial to promoting the safety, reliability and economy …

The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …

In real industries, there often exist application scenarios where the target domain holds fault
categories never observed in the source domain, which is an open-set domain adaptation …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …

Event-based cameras are the emerging bioinspired technology in vision sensing. Different
from the traditional standard cameras, the event-based cameras asynchronously record the …

The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …