Blades play a vital role in wind turbine system performances. However, they are susceptible
to damage arising from complex and irregular loading or even cause catastrophic collapse …

The most important parts of rotating machinery are the rolling bearings. Finding bearing
faults in time can avoid affecting the operation of the entire equipment. The data-driven fault …

Although the existing generative adversarial networks (GAN) have the potential for data
augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal …

In recent years, an increasing popularity of deep learning model for intelligent condition
monitoring and diagnosis as well as prognostics used for mechanical systems and …

Investigation of deep transfer learning on machinery fault diagnosis is helpful to overcome
the limitations of a large volume of training data, and accelerate the practical applications of …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

The entropy-based method has been demonstrated to be an effective approach to extract
the fault features by estimating the complexity of signals, but how to remove the strong …

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating
machinery is vital in manufacturing system, which can prevent catastrophic failure and …

Rotating machinery is widely applied in various types of industrial applications. As a
promising field for reliability of modern industrial systems, early fault diagnosis (EFD) …

The growing complexity of data derived from Industrial Internet of Things (IIoT) systems
presents substantial challenges for traditional machine-learning techniques, which struggle …