With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …

Meta-learning has effectively addressed the limit of deep learning fault diagnosis models
that demands a large number of samples. However, existing meta-learning models lack the …

The problem of practical open-set domain adaptation diagnosis has gained great attention
considering unobserved fault categories in target domain. However, existing studies assume …

The acknowledged challenge of intelligent fault diagnosis methods is that constructing a
reliable diagnosis model requires numerous labeled datasets as training data, which is …

Multisensory data-driven remaining useful life (RUL) prediction based on deep learning
techniques is gaining increasing popularity as it captures the degradation process of …

To reveal the mechanism of gear meshing induced rubbing and investigate the nonlinear
dynamic response characteristics of a gear-dual-rotor system with multi-position rubbing, a …

Considering the calculation efficiency and accuracy of meshing characteristics of gear pair
with tooth root crack fault, the parametric model of cracked spur gear is established by …

In a planetary gear train (PGT), the accuracy of gear tooth crack fault monitoring is affected
by many external factors, such as the radial assembly error of the output shaft. In this paper …

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating
machinery's safety and effective operation. The success of most current methods relies on …

Intelligent machinery prognostics and health management (PHM) methods have been
attracting growing attention in the past years, with the rapid development of the artificial …