Bearing faults of rotating machinery are common compound faults, and diverse fault
categories are coupled, which makes it challenging to achieve state monitoring. For this …

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized
in the fault diagnosis field. However, there are two common problems in deep-learning …

Bearings prevent damage caused by frictional forces between parts supporting the rotation
and they keep rotating shafts in their correct position. However, the continuity of work under …

Rolling bearing is one of the main components of rotating machinery, timely and accurate
fault diagnosis plays an important role in the reliability and safety of modern industrial …

Abstract Machine learning techniques have had great success in fault diagnosis. However,
the traditional machine learning methods rely heavily on manual priori knowledge leading to …

In actual industrial application scenarios, noise pollution makes it difficult to extract fault
features accurately via conventional methods. A novel method for rolling bearing fault …

Deep learning based intelligent fault diagnosis methods have become a research hotspot in
the fields of fault diagnosis and the health management of rolling bearings in recent years …

Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent
years due to the promising development of artificial intelligent technology. Many intelligent …

Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in
practical engineering, the fault modes of rolling bearings are usually compound faults and …

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 …