Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …

In the recent years, deep learning-based intelligent fault diagnosis methods of rolling
bearings have been widely and successfully developed. However, the data-driven method …

In recent years, deep learning has been proved to be a promising bearing fault diagnosis
technology. However, most of the existing methods are based on single-task learning. Fault …

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 …

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 …

High precision and fast fault diagnosis is an important guarantee for the safe and reliable
operation of machinery. In recent years, due to the strong recognition ability, data-driven …

Accurate and real-time fault diagnosis (FD) and working conditions identification (WCI) are
the key to ensuring the safe operation of mechanical systems. We observe that there is a …

The health condition of a wheelset bearing, the key component of a railway bogie, has a
considerable impact on the safety of a train. Traditional bearing fault diagnosis techniques …

Fault diagnostic methods based on deep learning achieve impressive progress recently, but
most studies assume that signals from the source domain and target domain share a similar …

Deep neural networks highly depend on substantial labeled samples when identifying
bearing fault. However, in some practical situations, it is very difficult to collect sufficient …