Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection

A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristic...

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Bibliographic Details
Published in:MATEC web of conferences 2020, Vol.327, p.3003
Main Authors: Li, Hui, Liu, Xuhan
Format: Article
Language:English
Subjects:
Decomposition
Fault detection
Fault diagnosis
Gearboxes
Kurtosis
Mathematical analysis
Roller bearings
Signal processing
Spectra
Vibration
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Summary:A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202032703003