Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …

Semisupervised learning holds significant academic and practical importance in the realm of
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …

In recent studies, Transformer collaborated with convolution neural network (CNN) have
made certain progress in the field of intelligent fault diagnosis by leveraging their respective …

Recent studies on machinery anomaly detection only based on normal data training models
have yielded good results in improving operation reliability. However, most of the studies …

The fault diagnosis of rolling bearings with imbalanced data has always been a particularly
challenging problem. With data augmentation methods to complement the imbalanced …

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …

Although data-driven methods have been widely used in planetary gearbox fault diagnosis,
the difficulty and high cost of manual labeling leads to little labeled training data, which limits …

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

Although data-driven model has achieved remarkable results in gearbox fault diagnosis, its
diagnostic accuracy is still highly dependent on large amounts of high-quality labeled …

In practical engineering scenarios, the operating speed of mechanical equipment is intricate
and variable. However, much of the existing research on intelligent fault diagnosis is …