Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

As one of the most immensely growing renewable energies, the wind power industry also
experiences a high failure rate and operation & maintenance cost. Therefore, the condition …

Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …

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 …

For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional
machine learning-based methods generally provide insufficient feature representation and …

Rotating machines have been widely used in industrial engineering. The fault diagnosis of
rotating machines plays a vital important role to reduce the catastrophic failures and heavy …

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) …

Vibration signals are utilized widely for machinery fault diagnosis. These typical deep neural
networks (DNNs), eg, convolutional neural networks (CNNs) perform well in feature learning …

Fault diagnosis of rotating machinery serves an important role in informing system operation
and predictive maintenance decisions. To quantify the fault information from vibrational …

Entropy, as a complexity measure, has been widely applied for time series analysis. One
preeminent example is the design of machine condition monitoring and industrial fault …