Deep learning has been widely used nowadays to achieve an automated fault diagnosis of
rolling bearings. However, most of deep learning based bearing fault diagnosis methods are …

Rolling bearings are the vital components of rotary machines. The collected data of rolling
bearing have strong noise interference, massive unlabeled samples, and different fault …

Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …

Recent works suggest that using knowledge transfer strategies to tackle cross-domain
diagnosis problems is promising for achieving engineering diagnosis. This article presents a …

Bearing fault diagnosis in real-world applications has challenges such as insufficient
labeled data, changing working conditions of the rotary machinery, and missing data due to …

Intelligent diagnosis is one of the key points of research in the field of bearing fault
diagnosis. As a representative unsupervised data expansion method, generative adversarial …

Transfer learning based on a single source domain to a target domain has received a lot of
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …

The data imbalance limits the stability and accuracy in fault diagnosis of rolling bearings. In
general, traditional methods need the necessary features and a large number of labeled …

Traditional data-driven intelligent fault diagnosis methods for rotating component commonly
assume that sufficient labeled data is available. However, the rotary machine works in a …

Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …