Spectral kurtosis optimization for rolling element bearings

Abstract

Spectral kurtosis (SK) represents a valuable tool for extracting transients buried in noise, which makes it very powerful for the diagnostics of rolling element bearings. However, SK requires the selection of a time-frequency frame for decomposition, so that the kurtosis of each frequency slot can be estimated over time. This paper proposes a technique to optimise SK for diagnostics of rolling element bearings. This technique involves two steps. First, the power spectral density of the signal is prewhitened using an autoregressive model. Second, the prewhitened signal (consisting of noise and transients) is decomposed using complex Morlet wavelets. The complex Morlet wavelet is used as a filter bank with constant proportional bandwidth (uniform resolution on a logarithmic frequency scale). Different banks are used to select the best filter for the envelope analysis - in terms of centre frequency and bandwidth - as the one that maximizes the SK.