With the rapid development of pattern recognition represented by deep learning, the
massive excellent bearing fault diagnosis methods have emerged. However, the majority of …

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is
very important for its stable operation. The information fusion of multisensor data has been a …

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 …

With the development of technologies such as Internet of Things and big data, the realization
of fusion and cross analysis of multi-sensor signals provides the possibility for …

Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery.
Existing methods use signals from a single dimension, limiting diagnostic generality and …

Condition monitoring and fault diagnosis of the bearing are essential for the smooth
operation of rotating machinery. In this paper, an end‐to‐end intelligent fault diagnosis …

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 …

Deep learning has been an important topic in fault diagnosis of motor bearings, which can
avoid the need for extensive domain expertise and cumbersome artificial feature extraction …

Rolling bearings are the core components of rotating machinery. Their health directly affects
the performance, stability and life of rotating machinery. To prevent possible damage, it is …

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