MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, China
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Entropy 2019, 21(4), 331; https://doi.org/10.3390/e21040331
Received: 24 February 2019
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Revised: 21 March 2019
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Accepted: 24 March 2019
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Published: 27 March 2019
(This article belongs to the Collection Wavelets, Fractals and Information Theory)
In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.
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Keywords:
modified variational mode decomposition; multipoint optimal minimum entropy deconvolution adjusted; Teager energy operator demodulation; fault feature extraction; rolling bearings
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MDPI and ACS Style
Li, Z.; Ma, J.; Wang, X.; Wu, J. MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings. Entropy 2019, 21, 331. https://doi.org/10.3390/e21040331
AMA Style
Li Z, Ma J, Wang X, Wu J. MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings. Entropy. 2019; 21(4):331. https://doi.org/10.3390/e21040331
Chicago/Turabian StyleLi, Zhuorui, Jun Ma, Xiaodong Wang, and Jiande Wu. 2019. "MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings" Entropy 21, no. 4: 331. https://doi.org/10.3390/e21040331
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