Helicopter Bearing Degradation Data Analysis Using Spectral Kurtosis and Time-Frequency Representation

Abstract

Rolling element bearing is crucial for operation safety of modern mechanical equipment. Its poor damping and heavy load capacity usually makes it degrade faster than the shafts and the gear sets. In this paper, a recently proposed time-frequency (TF) representation tool, named the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is introduced for the TF representation of helicopter bearing degradation data. Totally three sets of sequentially collected accelerometer data were included for the comparison study, with the first two data sets showing no signs of degradation and the third data set corresponds to totally scored bearing rollers. Facilitated by CEEMDAN method, no clear distinction can be observed on the TF plane as for the strong background noises. Thus we previously highlight the bearing signatures using spectral kurtosis (SK), a forth order statistics which is sensitive for impulsive components. Its fast algorithm, called the kurtogram, implements the detection of considerably weak bearing components and suppresses other background components, e.g., gear mesh components. With the preprocessing by kurtogram, the feature information on the TF plane can be easily characterized for the third data set.