Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Abstract

Condition-based maintenance via vibration signal processing plays an important role to reduce unscheduled machine downtime and avoid catastrophic accidents in industrial enterprises. Many machine faults, such as local defects in rotating machines, manifest themselves in the acquired vibration signals as a series of impulsive events. The spectral kurtosis (SK) technique extends the concept of kurtosis to that of a function of frequency that indicates how the impulsiveness of a signal. This work intends to review and summarize the recent research developments on the SK theories, for instance, short-time Fourier transform-based SK, kurtogram, adaptive SK and protrugram, as well as the corresponding applications in fault detection and diagnosis of the rotating machines. The potential prospects of prognostics using SK technique are also designated. Some examples have been presented to illustrate their performances. The expectation is that further research and applications of the SK technique will flourish in the future, especially in the fields of the prognostics.