Roller element bearing fault detection and diagnosis based on an optimised envelope analysis

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is
very important in the field of machinery health monitoring for safe and efficient operations. Currently,
envelope analysis is widely used to obtain the bearing defect harmonics from the envelope signal
spectrum for the detection and diagnosis and has shown good results in identifying incipient faults
occurring on the different parts of a bearing. However, a critical step in implementing envelope
analysis is to determine a frequency band that contains faulty bearing signal component with highest
signal to noise. Conventionally, the choice of the band is made by manual spectrum comparison via
identifying the resonance frequency where the largest change occurred. This paper presents a
spectral kurtosis (SK) based method to determine optimum envelope analysis parameters including
the filtering band and centre frequency through a short time Fourier transform (STFT). The results
show that the maximum amplitude of the kurtogram provides the optimal parameters for band pass
filter which allows both small outer race fault and large inner race fault to be determined from
optimised envelope spectrum.