Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

Abstract Domain adaptation-based fault diagnosis (DAFD) methods have been explored to
address cross-domain fault diagnosis problems, where distribution discrepancy exists …

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

Generalizing deep models to unseen working conditions is an essential topic for intelligent
fault diagnosis. Existing domain generalization-based fault diagnosis (DGFD) methods …

Data-driven fault diagnosis has been a hot topic in recent years with the development of
machine learning techniques. However, the prerequisite that the training data and the test …

Recent years have witnessed the successful development of using knowledge transfer
strategies to tackle cross-domain fault diagnosis problems. Most existing studies generally …

Abstract Domain generalization methods can effectively identify machinery faults under
unseen new target working conditions. Nevertheless, most of them rely on data from multiple …

Data-driven fault diagnosis plays a key role in stability and reliability of operations in modern
industries. Recently, deep learning has achieved remarkable performance in fault …

Recently, cross-domain fault diagnosis methods have been successfully developed and
applied. Among them, the ones exhibiting the best performance rely on the common …

Recent years have witnessed the successful development of domain adaptation methods to
tackle cross-domain fault diagnosis problems. However, these methods require the target …