In industrial production, it is particularly important to diagnose the bearing fault in time under
variable loads. The intelligent diagnosis method has strong robustness without human …

Traditional methods of rolling bearing fault diagnosis generally have the following
disadvantages: low accuracy of fault severity identification, the need for artificial feature …

In recent years, deep learning methods, especially convolutional neural network (CNN),
have received extensive attentions and applications in fault diagnosis. However, recent …

As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has
been a research hotspot. In order to diagnose the faults of rolling bearing under different …

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing
attention due to the availability of massive condition monitoring data. However, most existing …

Aiming at automatic feature extraction and fault recognition of rolling bearings, a new data-
driven intelligent fault diagnosis approach using multi-head attention and convolutional …

In recent years, deep learning has achieved great success in bearing fault diagnosis due to
its robust feature learning capabilities. However, in the actual industry, the diagnostic …

Bearings are the key and important components of rotating machinery. Effective bearing fault
diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to …

Accurate fault information is critical for optimal scheduling of production activities, improving
system reliability, and reducing operation and maintenance costs. In recent years, many fault …

With the rapid development of fault prognostics and health management (PHM) technology,
more and more deep learning algorithms have been applied to the intelligent fault diagnosis …