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An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Author

Listed:
  • Guoping An

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
    Center of Safety Technology, National Railway Administration, Beijing 100166, China)

  • Qingbin Tong

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yanan Zhang

    (State Grid JIBEI Electric Power Co., Ltd. Maintenance Branch State Grid, Beijing 102488, China)

  • Ruifang Liu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Weili Li

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Junci Cao

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yuyi Lin

    (Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, USA)

Abstract

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

Suggested Citation

  • Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin, 2021. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing," Energies, MDPI, vol. 14(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1079-:d:501531
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    References listed on IDEAS

    as
    1. Cong Wang & Meng Gan & Chang’an Zhu, 2018. "Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 937-951, April.
    2. Wentao Huang & Fanzhao Kong & Xuezeng Zhao, 2018. "Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1257-1271, August.
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    Cited by:

    1. Zhenen Li & Xinyan Zhang & Tusongjiang Kari & Wei Hu, 2021. "Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings," Energies, MDPI, vol. 14(15), pages 1-19, July.

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