Fault diagnosis of bearing under variable working conditions is widely required in practice,
and the combination of working conditions and fault fluctuations increases the complexity of …

Most fault identification methods based on deep learning rely on a large amount of data, and
their effects are limited in the actual production environment. In the case of multiple …

Meta-learning has effectively addressed the limit of deep learning fault diagnosis models
that demands a large number of samples. However, existing meta-learning models lack the …

Deep learning-based bearing fault diagnosis has been systematically studied in recent
years. However, the success of most of these methods relies heavily on massive labeled …

Rolling bearing fault diagnosis with unlabeled data is a meaningful yet challenging task.
Recently, deep transfer learning methods with maximum mean discrepancy (MMD) have …

Deep learning for bearing fault diagnosis often requires a large quantity of comprehensive
data to give support in the field of rotating machinery fault diagnosis. However, large …

Fault diagnosis with transfer learning has achieved great attention. However, existing
methods mostly focused on single-source-single-target sceneries. In some cases, there may …

Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …

Deep learning based fault diagnosis methods assume that training and testing data with
sufficient labels are available and share a same distribution. In practical scenarios, this …