scholarly journals Application of resonance demodulation method based on local characteristic-scale decomposition and spectral kurtosis in gearbox fault

2019 ◽  
Vol 657 ◽  
pp. 012050
Author(s):  
R Cong ◽  
C H Li ◽  
J Q Wu
Keyword(s):  
Local Characteristic ◽  
Characteristic Scale ◽  
Spectral Kurtosis ◽  
Demodulation Method

scholarly journals Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shuting Wan ◽  
Xiong Zhang ◽  
Longjiang Dou
Keyword(s):  
Resonant Frequency ◽  
Frequency Band ◽  
Practical Applications ◽  
Noise Interference ◽  
Spectral Kurtosis ◽  
Modified Method ◽  
Weak Fault ◽  
Fault Information ◽  
Demodulation Method ◽  
Fault Characteristic

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.

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