Generative adversarial networks (GANs) have shown promise in the field of small sample
fault diagnosis. However, it is worth noting that generating synthetic data using GANs is time …

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 …

Deep learning (DL) fault diagnosis methods require no expert knowledge and can
adaptively extract fault features to realize automated diagnoses. However, factories' limited …

The performance of intelligent fault diagnosis models heavily depends on the amount of
monitoring data available. In the situations of monitoring data insufficient for fault diagnosis …

This paper introduces Con-GAN, an innovative improvement of GAN-based data
augmentation designed to address data insufficiency in fault diagnosis methodologies …

Fault diagnosis based on deep learning has become a research hotspot. However, the
classification accuracy may be low if the amount of training data is insufficient. To solve this …

Fault diagnosis plays the increasingly vital role to guarantee the machine reliability in the
industrial enterprise. Among all the solutions, deep learning (DL) methods have achieved …

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical
equipment. With the rapid development of deep learning technology, the methods based on …

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

Modern industrial fields utilize complex mechanical equipment and machinery, which are
closely linked, and equipment faults are difficult to express. Therefore, fault diagnosis is …