Abstract
The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature by the envelope demodulation method. However, in practical applications, the fault source may be located in different resonant frequency bands; plus in noise interference, the weak side of the compound fault is not easy to be identified by the FSK. In order to improve the accuracy of fast spectral kurtosis analysis method, a modified method based on maximum correlation kurtosis deconvolution (MCKD) is proposed. According to the possible fault characteristic frequencies, the period of MCKD is calculated, and the appropriate filter length is selected to filter the original compound fault signal. In this way, the compound fault located in different resonance bands is separated. Then, the signal after MCKD filtering is analyzed by FSK. Through the simulation and experimental analysis, the MCKD can separate the compound fault information in different frequency band and eliminate the noise interference; the FSK can accurately identify the resonance frequency and identify the weak fault characteristics of compound fault.
Highlights
The fault characteristics of rotating machinery have certain regularity and periodicity
The performance of ELMD often heavily depends on proper selection of its model parameters; to overcome this shortcoming, Zhang et al propose an optimized ensemble local mean decomposition method to determinate an optimum set of ELMD parameters for vibration signal analysis [10]
(c) The spectral kurtosis of inner ring fault (d) The envelope spectrum after fast spectrum kurtosis (FSK) filtering of its own algorithm, it is not possible to accurately extract multiple fault information located in different resonance frequency bands
Summary
The fault characteristics of rotating machinery have certain regularity and periodicity. Hemmati et al proposed a modified and effective signal processing algorithm to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes [9]. Fast spectral kurtosis is sensitive to noise, and it is easy to cause misdiagnosis or missed diagnosis for compound fault in different resonance frequency bands. For most cases, the compound fault has a large difference in the kurtosis values of the two resonance band components. This results in omission of part of the fault information when dealing with compound failure problems with FSK. The simulation signal with the impact characteristics located at two different resonance bands is defined as y = A 1e−ξ[t−q1(t)/f1]2 ⋅ sin (2πfn1t) + A 2e−ξ[t−q2(t)/f2]2.
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