The vibration signals of rotating machinery present a strongly non-linear and non-Gaussian
behavior, and bispectrum is well suitable to analyze this kind of signals. Due to modulation …

Rotating machinery is one of the most typical types of mechanical equipment and plays a
significant role in industrial applications. Condition monitoring and fault diagnosis of rotating …

Empirical mode decomposition (EMD) has been widely applied to analyse signals for the
detection of faults in rotating machinery. However, sometimes, it cannot reveal signal …

Bispectrum is a powerful tool for non-Gaussian signal processing and nonlinearity detection.
However, it is difficult to use in practical applications due to that it is a 2-dimensional …

Rolling bearings are vital components in the induction motors. Rolling bearings are
encountered to extremely tensions, and their faults can cause serious damages to induction …

To accurately extract the fault characteristics of vibration signals of rotating machinery is of
great significance to the unit online monitoring and evaluation. However, because the …

A rolling element bearing fault recognition approach is proposed in this paper. This method
combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data …

Rolling bearings are extremely critical rotating mechanical components, and when they fail,
they can damage the equipment, causing safety threats or economic losses. Collecting and …

Over the years, condition monitoring of rotating machines has been extensively applied for
enhancing equipment reliability and maintenance cost-effectiveness, through the early …

The rolling element bearing is a key part in many mechanical equipment. The accurate and
timely diagnosis of its faults is critcal for predictive maintenance. Vibration signals from a …