Feature Extraction of Rolling Bearing Based on SVD and MED
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Published
2024-05-20
Issue Date
2025-06-05
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.