The past years have witnessed the successful development of intelligent machinery fault
diagnostic methods. Besides the basic data-driven fault diagnosis tasks where the training …

Despite the recent success in data-driven machinery fault diagnosis, cross-domain
diagnostic tasks still remain challenging where the supervised training data and …

Recently, deep learning-based intelligent fault diagnosis techniques have obtained good
classification performance with amount of supervised training data. However, domain shift …

In the past years, deep learning-based machinery fault diagnosis methods have been
successfully developed, and the basic diagnostic problems have been well addressed …

In the recent years, data-driven machinery fault diagnostic methods have been successfully
developed, and the tasks where the training and testing data are from the same distribution …

Data-driven machinery fault diagnosis methods have been successfully developed in the
past decades. However, the cross-domain diagnostic problems have not been well …

Domain generalization-based fault diagnosis has recently emerged to address domain shift
problems. Most existing methods learn domain-invariant representations from multiple …

Recently, substantial research has explored the development of deep-learning-based
methods to diagnose faults in rotating machinery. For these diagnosis methods, it is difficult …

Data driven-based intelligent fault diagnosis methods, as a promising approach, have been
widely employed in the health management and maintenance decision of rotating …

Despite the recent success of deep-learning-based fault diagnosis of rotating machinery, to
enable accurate and robust diagnosis models, existing approaches proceed with the …