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 …

The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

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

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary
mechanical system. In practice, the sample proportion between faulty data and healthy data …

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of
engineering equipment. Many intelligent diagnosis methods have been successfully …

Convolutional neural networks (CNNs) are being utilized for mechanical fault diagnosis, due
to its excellent automatic discriminative feature learning ability. However, the poor …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …