With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

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 …

Unsupervised domain adaptation (UDA)-based methods have made great progress in
bearing fault diagnosis under variable working conditions. However, most existing UDA …

Deep learning has gained significant success in fault diagnosis. However, the number of
gearbox health samples is inevitably much larger than that of fault samples in real-world …

With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …

The application of convolutional neural network (CNN) has greatly promoted the scope and
scenario of intelligent fault diagnosis and brought about a significant improvement of …

With the rapid development of pattern recognition represented by deep learning, the
massive excellent bearing fault diagnosis methods have emerged. However, the majority of …