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 …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

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

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

Problems such as small samples and variable working conditions arise in complex practical
mechanical fault diagnosis scenarios. Although the domain-adaptive method has an …

Intelligent diagnosis is one of the key points of research in the field of bearing fault
diagnosis. As a representative unsupervised data expansion method, generative adversarial …

To date, various meta-learning methods have been explored to face the data-scarcity
problem in fault diagnosis. Almost without exception, these methods work on the premise …

Effective fault diagnosis on rotating machinery is crucial for ensuring the reliability and safety
of mechanical equipment. However, available fault data are frequently scarce in real …