Feature Extraction of Rolling Bearing Based on SVD and MED

HE Zeren, PENG Zhenrui

Control Engineering of China ›› 2024, Vol. 31 ›› Issue (5) : 884-890.

Control Engineering of China ›› 2024, Vol. 31 ›› Issue (5) : 884-890.

Feature Extraction of Rolling Bearing Based on SVD and MED

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Abstract

For the problem that the vibration signal of rolling bearing is easily affected by noise and it is difficult to extract fault feature information, a bearing fault diagnosis method based on singular value decomposition(SVD) and reconstruction combined with minimum entropy deconvolution(MED) enhancement is proposed. Firstly, the original signal is decomposed by SVD, and the linear kurtosis corresponding to the singular component is calculated. Secondly, the singular component (SC) are selected according to the linear kurtosis with the set threshold, superimposing to get the reconstructed signal. Thirdly, the reconstructed signal is enhanced by MED to protrude the periodic shock components in the signal. Finally, the fault characteristic frequency is extracted by envelope demodulation. The results of both simulated signal and measured signal show that this method can reduce the influence of noise on vibration signal, highlight fault characteristic information, and realize fault diagnosis.

Key words

Singular value decomposition / minimum entropy deconvolution / L-kurtosis / fault feature extraction

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HE Zeren, PENG Zhenrui. Feature Extraction of Rolling Bearing Based on SVD and MED[J]. Control Engineering of China, 2024, 31(5): 884-890

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