The use of spectral kurtosis as a trend parameter for bearing faults diagnosis

Abstract

Vibration signals are widely used in the health monitoring of rolling element bearings. A critical work of the bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing (inner race, outer race, cage, as well as balls). However, a main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In This paper, we present a spectral kurtosis based method to determine optimum envelope analysis parameters including the filtering band and centre frequency through a short time Fourier transform. In the literature, spectral kurtosis is mainly presented as a tool used to detect non-stationary components in a signal. The results show that the maximum amplitude of the kurtogram (ways to compute the spectral kurtosis) provides the optimal parameters for band pass filter which allows both small outer race fault and large inner race fault to be determined from optimized envelope spectrum.