Owing to the harsh operating environment of rolling bearings, acquired vibration signals
contain strong noise interference, which makes it challenging for conventional methods to …

The vibration signals collected from rolling bearings in industrial systems are highly complex
and contain intense environmental noise, which challenges the performance of traditional …

Convolutional neural networks (CNNs) have demonstrated promising effectiveness in
vibration-based fault diagnosis. However, the faulty characteristics are usually distributed on …

In recent years, deep learning has shown great vitality in the field of intelligent fault
diagnosis. However, most diagnostic models are not yet capable enough to capture the rich …

Due to rolling bearings usually operate under fluctuating working conditions in practical
engineering, the raw vibration signals generated by bearing faults have nonlinear and non …

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention.
However, for most cases, the success of machine learning-based models relies on the …

Deep learning (DL)-based rolling bearing fault diagnosis method has made significant
achievements, but its diagnostic performance is still limited by few samples. Aiming at this …

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 …

Existing diagnosis methods for bearing faults often neglect the temporal correlation of
signals, resulting in easy loss of crucial information. Moreover, these methods struggle to …

Intelligent algorithms based on convolutional neural network (CNN) has demonstrated
remarkable potential in diagnosing bearing faults. However, Accurate and robust fault …