Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. (January 2016) - Wang, Yanxue; Xiang, Jiawei; Markert, Richard; Liang, Ming
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. Highlights: This work reviews the recent research developments on the SK theories. The applications of SK in fault detection, diagnosis of the rotating machines are summarized. Time–frequency decomposition methods used in the improved SK techniques are introduced. Major methods combined with a SK technique for bearing fault detection were also given.