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

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

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 …

With the assumption of sufficient labeled data, deep learning based machinery fault
diagnosis methods show effectiveness. However, in real-industrial scenarios, it is costly to …

Planetary gearbox has complex structures and works under various non-stationary
operating conditions. The vibration signals of planetary gearbox are complicated and …

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 …

Unsupervised domain adaptation alleviates the dependencies of conventional fault
diagnosis methods on sufficient labeled data and strict data distributions. Nonetheless, the …

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 …

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 …

The fault diagnosis is very important for the modern industry. Due to machine working
conditions changing frequently, most of current fault diagnosis models built on the training …