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 …

Imbalance is a typical characteristic of data in the field of intelligent fault diagnosis. As a data
augmentation method that both balances data and extends information, the generative …

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

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 …

In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …

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 …

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

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 …

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

Intelligent fault diagnosis with imbalanced data is a problem that often raises concerns. The
diagnosis is more effective when the imbalanced dataset is supplemented with data …