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

Rotating machines is an essential part of any manufacturing industry. The sudden
breakdown of such machines due to improper maintenance can also lead to the industries' …

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

Abstract Domain generalization-based fault diagnosis (DGFD) has garnered significant
attention due to its ability to generalize prior diagnostic knowledge to unseen working …

In the field of mechanical fault diagnosis, most of the collected signals are normal signals,
leading to data imbalance and reduction of fault diagnosis performance. To address the …

Fault diagnosis of rolling bearings plays a pivotal role in modern industry. Most existing
methods have two disadvantages: 1) The assumption that the training and test data obey the …

In solving the data imbalance problem, most of the existing studies ignored the effect of the
number of samples on the diagnostic performance of intelligent fault diagnostic models …

Whether in the industrial, medical, or real-world domains, more and more data are being
collected. The common particularity of all these application domains is that a great part of …

Fault diagnosis of rotating machines is essential for the safe and efficient operation of
maritime vessels. It prevents potential failures in rotating machines in maritime systems …

A start-up company is usually only able to collect normal samples, resulting in extreme label
shortage and inability to establish effective intelligent diagnostic models. Especially for …