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Noise reduction method of bearing vibration signal based on parameter optimized VMD-wavelet thresholding

Published:2024-03-06 author:YAN Haipeng, HAO Xinyu, QIN Zhiying. Browse: 513 Check PDF documents

Noise reduction method of bearing vibration signal based on parameter

optimized VMD-wavelet thresholding

YAN Haipeng, HAO Xinyu, QIN Zhiying

(School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract: Aiming to solve the problem of random noise in rolling bearing vibration signals under complex working conditions, a parametric optimized variational modal decomposition(VMD)-wavelet thresholding method for noise reduction was proposed. Firstly, using envelope entropy as the fitness function, the modal decomposition number K and the penalty factor α of the variational modal decomposition algorithm were adaptively selected using the Aquila Optimizer algorithm, and brought into the VMD decomposition to obtain a number of intrinsic mode functions (IMFs). Then, the IMF components were divided into pure and noise-containing components based on the crag-correlation coefficient, and the noise-containing components were subjected to wavelet thresholding for noise reduction. Finally, the processed components were reconstructed and subjected to envelope spectral analysis with reconstructed signal to achieve signal noise reduction in rolling bearings, which were verified using simulated signals and publicly available bearing datasets from Case Western Reserve University. The results show that the noise reduction method based on parameter optimized VMD-wavelet thresholding reduces the random noise under the operating condition of rolling bearings, and the signal-to-noise ratio of the simulated signal is improved by 53%, and the mean-square error is reduced by 13% relative to that of the wavelet-thresholding noise reduction method; when the fault characteristic frequency is 162Hz, the first 6-fold spectral peaks of the envelope spectrum of the resulting experimental noise reduction signal are more pronounced and less affected by random noise. The research method is informative in signal noise reduction in rotating machinery such as rolling bearings.
Key words: rolling bearing; fault diagnosis; variational modal decomposition(VMD); intrinsic mode function; wavelet thresholding noise reduction;aquila optimizer(AO); kurtosis-correlation coefficient

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