Abstract
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.
Highlights
Bearings are important mechanical components in various mechanical equipments, such as automobile, metallurgical equipment, etc
This paper put forwards an approach of bearing fault monitoring and recognition based on empirical mode decomposition (EMD) and spectrum kurtosis based envelope spectrum
The envelope spectrum method based on spectral kurtosis can effectively extract the feature frequencies related to bearing defects
Summary
Bearings are important mechanical components in various mechanical equipments, such as automobile, metallurgical equipment, etc. Among many methods of bearing fault feature extraction, the approach based on vibration signal analysis technique is widely utilized because of its high reliability. Fast Fourier transform (FFT), power spectrum estimation, envelope spectrum and cepstrum analysis technique have achieved good results in bearing fault detection and diagnosis [1,2]. The rolling bearing system defect recognition and diagnosis need to study new vibration analysis technology. This paper put forwards an approach of bearing fault monitoring and recognition based on EMD and spectrum kurtosis based envelope spectrum. The envelope spectrum method based on spectral kurtosis can effectively extract the feature frequencies related to bearing defects. Experimental results show that this approach can effectively monitor and identify rolling bearing faults
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