In the last decade, predictive maintenance has attracted a lot of attention in industrial
factories because of its wide use of the Internet of Things and artificial intelligence …

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 …

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 …

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 …

Effective fault diagnosis is essential for maintaining the safe running of machine systems.
Recently, the data-driven methods have shown great potential in intelligent fault diagnosis …

Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …

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 …

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

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 …

Deep learning is widely used in fault diagnosis of mechanical equipment and has achieved
good results. However, these deep learning models require a large number of labeled …