Transformer has been widely applied in the research of rotating machinery fault diagnosis
due to its ability to explore the internal correlation of vibration signals. However, challenges …

The deep learning-based fault diagnosis approaches have shown great advantages in
ensuring rotating machinery (RM) work normally and safely. However, in real industrial …

Aiming at the problems of low accuracy and robustness of traditional deep learning fault
diagnosis methods, a novel attention-based multi-feature parallel fusion model …

Deep learning (DL) has attained remarkable achievements in diagnosing faults for rotary
machineries. Capitalizing on the formidable learning capacity of DL, it has the potential to …

Anomaly detection (AD) is an important task of machines' condition monitoring (CM). Data-
driven policies can be used in a more intelligent way to achieve anomaly detection and …

Intelligent fault diagnosis has made significant progress in recent years. However, due to the
following two difficulties, these solutions are still difficult to implement: 1) The majority of …

Deep learning methods have demonstrated promising performance in fault diagnosis tasks.
Although the scarcity of data in industrial scenarios limits the practical application of such …

Condition monitoring (CM) plays a vital role in machine maintenance for ensuring the
system's operating reliability and safety as fault detection and health degradation …

Rotating machinery (RM) such as bearings and gears often operates under time-varying
operating conditions (TVOC), which makes the vibration signals non-stationary. In this case …

The service conditions of wheelset bearings has a direct impact on the safe operation of
railway heavy haul freight trains as the key components. However, speed fluctuation of the …