Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …

In practical scenarios, it is difficult to acquire fault data from rotating machinery, resulting in
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …

Deep learning has been widely applied to intelligent fault diagnosis with balanced training
set. However, certain available fault data are extremely limited, resulting in an imbalanced …

The realization of accurate fault diagnosis is crucial to ensure the normal operation of
machines. At present, an intelligent fault diagnosis method based on deep learning has …

The class imbalance problem has a huge impact on the performance of diagnostic models.
When it occurs, the minority samples are easily ignored by classification models. Besides …

Intelligent fault diagnosis based on deep neural networks and big data has been an
attractive field and shows great prospects for applications. However, applications in practice …

Data imbalance is a common problem in rotating machinery fault diagnosis. Traditional data-
driven diagnosis methods, which learn fault features based on balance dataset, would be …

The collected data from industrial machines are often imbalanced, which poses a negative
effect on learning algorithms. However, this problem becomes more challenging for a mixed …

Fault diagnosis in an open world refers to the diagnosis tasks that need to cope with
previously unknown faults in the online stage. It faces a great challenge yet to be addressed …

With the lack of failure data, class imbalance has become a common challenge in the fault
diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced …