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 …

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

Gearbox has a compact structure, a stable transmission capability, and a high transmission
efficiency. Thus, it is widely applied as a power transmission system in various applications …

Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …