The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …

As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize
the adversarial learning of the feature extractor and domain discriminator to extract the …

Accurate and fast rolling bearing fault diagnosis is required for the normal operation of
rotating machinery and equipment. Although deep learning methods have achieved …

Intelligent fault diagnosis methods based on deep learning have attracted significant
attention in recent years. However, it still faces many challenges, including complex and …

Fault diagnostic methods based on deep learning achieve impressive progress recently, but
most studies assume that signals from the source domain and target domain share a similar …

It is generally difficult to obtain a large number of labeled samples (ie, samples with known
fault types) of rolling bearings installed on large-scale mechanical equipment under current …

The research of intelligent fault diagnosis method has made great progress. The
prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant …

Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …

Numerous intelligent methods have been developed to approach the challenges of fault
diagnosis. However, due to the different distributions of training samples and test samples …

At present, most of the intelligent fault diagnosis methods of rolling element bearings require
sufficient labeled data for training. However, collecting labeled data is usually expensive …