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 …

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 …

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 …

In data-driven bearing fault diagnosis, it is unrealistic to obtain enough labeled data, and the
data used for training and testing often have different distributions. Existing methods typically …

In recent years, great progress has been made in intelligent bearing fault diagnosis based
on transfer learning (TL). However, the huge number of parameters is ignored when using …

Feature extraction from a time sequence signal without manual information is an important
part for bearing intelligent diagnosis. With the merits of signal information and feature …

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 …

The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …

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

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 …