Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics


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Many machine faults, such as local defects in bearings and gears, manifest themselves in vibration signals as a series of impulsive events. Kurtosis is a measure of the impulsiveness of a signal, and spectral kurtosis (SK) gives an indication of how the kurtosis (of a bandpass filtered signal) varies with frequency. This not only gives an indication of the frequency bands to be processed, but can also be used to generate a filter to extract the most impulsive part of a signal. The first step in calculating SK is to perform a time/frequency decomposition of the signal, and then calculate the kurtosis for each frequency line. The paper compares the original STFT (short time Fourier transform) with wavelet analysis for the time/frequency decomposition, and for determining the optimum combination of centre frequency and bandwidth for maximizing the SK. The paper also describes how the SK can be enhanced by “prewhitening” the signal using an autoregressive (AR) model, this sometimes revealing an incipient fault at a much earlier stage.



Key Engineering Materials (Volumes 293-294)

Edited by:

W.M. Ostachowicz, J.M. Dulieu-Barton, K.M. Holford, M. Krawczuk and A. Zak






R.B. Randall "Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics", Key Engineering Materials, Vols. 293-294, pp. 21-32, 2005

Online since:

September 2005





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