Vibration signal-based methods have been widely utilized in machine fault diagnosis.
Usually, a lack of sufficient training data can prevent these methods from achieving …

In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to
train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper …

Generative adversarial networks (GANs) have been proved to be able to produce artificial
data that are alike the real data, and have been successfully applied to various image …

In actual engineering scenarios, limited fault data leads to insufficient model training and
over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic …

The fault diagnosis method based on generative adversarial networks (GANs) has been
successfully applied to the early fault detection of motor bearings, and it has effectively …

Intelligent fault diagnosis of machines has long been a research hotspot and has achieved
fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small …

As a useful data augmentation technique, generative adversarial networks have been
successfully applied in fault diagnosis field. But traditional generative adversarial networks …

Intelligent fault diagnosis has been a promising way for condition-based maintenance.
However, the small sample problem has limited the application of intelligent fault diagnosis …

Due to the real working conditions, the collected mechanical fault datasets are actually
limited and always highly imbalanced, which restricts the diagnosis accuracy and stability …

Intelligent fault diagnosis is of great significance to guarantee the safe operation of
mechanical equipment. However, the widely used diagnosis models rely on sufficient …