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Resonance-based sparse adaptive variational mode decomposition and its application to the feature extraction of planetary gearboxes

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  • Jing Zhu
  • Aidong Deng
  • Jing Li
  • Minqiang Deng
  • Wenqing Sun
  • Qiang Cheng
  • Yang Liu

Abstract

Due to the assumption that the VMD technique is essentially a set of adaptive Wiener filter banks and its performance depends to a large extent on the preset parameter K (the number of decomposition). A new method named resonance-based sparse adaptive variational mode decomposition (RSAVMD) is proposed for the decomposition of planetary gearbox vibration signals. Tunable Q-Factor Wavelet Transform (TQWT) and morphological component analysis (MCA) are introduced to decompose the original signal into high and low resonance components. High resonance components containing planetary gearbox signals are screened for analysis. At the same time, Quality factor is used to select the number of Variational mode decomposition (VMD) adaptively. This method was applied in fault diagnosis of planetary gearbox. Compared with VMD, RASVMD could extract fault characteristic frequency of planetary gearbox accurately, but VMD lost part of fault information, showing the superiority of RSAVMD. Simultaneously, the selection method of VMD decomposition number in literature was cited, and it was found that the decomposition number selected by the method in this paper was more accurate.

Suggested Citation

  • Jing Zhu & Aidong Deng & Jing Li & Minqiang Deng & Wenqing Sun & Qiang Cheng & Yang Liu, 2020. "Resonance-based sparse adaptive variational mode decomposition and its application to the feature extraction of planetary gearboxes," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    • Handle: RePEc:plo:pone00:0231540
    DOI: 10.1371/journal.pone.0231540
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    References listed on IDEAS

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    2. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
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