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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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310009
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meem_contribute@163.com
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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- Chinese Sci-tech Core Periodicals
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2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
