Fault diagnosis of rolling element bearing using more robust spectral kurtosis and intrinsic time-scale decomposition

Abstract

Spectral kurtosis (SK) has been proved to be a powerful tool to help extract impulse-like fault characteristics from original non-stationary vibration signals buried in strong masking noise. However, the SK coefficient based on the fourth sample moments suffers from a non-robustness problem, which may affect the accuracy of the SK-based kurtogram. Therefore, robust spectral kurtosis coefficients are defined based on quantiles in order to eliminate the influence of the outliers in original signals. Robust kurtograms are firstly utilized to analyze the fault signals. Subsequently, intrinsic time-scale decomposition followed by envelope demodulation is introduced to decompose the signal filtered by the kurtogram for fault detection. Compared with the conventional kurtogram and empirical mode decomposition, the results demonstrate that the improved method is able to facilitate the ability enhancement of the fault diagnosis of rolling element bearings.