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Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear

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  • He, Guolin
  • Ding, Kang
  • Wu, Xiaomeng
  • Yang, Xiaoqing

Abstract

In wind turbine planetary gearboxes, the sun gears are generally designed to float in the radial direction to achieve the uniform load distribution among different planet gears, but will lead to gear mesh errors and cause complicated vibration signals on the contrary. The floating sun gear is misdiagnosed as a distributed defect easily. Additionally, the floating sun gear's rotating motion and excitation force are too disorderly to be characterized by mathematical or empirical formulas. In the paper, a set of rigid-flexible coupling dynamics models of planetary gear train are built to research vibration characteristics of the floating sun gear under different conditions. Depend on the model-based vibration analysis, the rotating motion of floating sun gear is revealed well, which can be characterized by its frequency features, meanwhile mechanisms of modulation sidebands indicating different kinds of defects are deduced clearly. The experimental data keep good consistency with the simulation signals, which verifies the validity of the proposed well. Vibration feature indicators are obtained to distinguish fault signals and diagnose the distributed or localized defect of floating sun gear effectively, which is helpful to avoid the misdiagnosis of floating sun gear.

Suggested Citation

  • He, Guolin & Ding, Kang & Wu, Xiaomeng & Yang, Xiaoqing, 2019. "Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox with a floating sun gear," Renewable Energy, Elsevier, vol. 139(C), pages 718-729.
    • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:718-729
    DOI: 10.1016/j.renene.2019.02.123
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    References listed on IDEAS

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    1. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    2. Li, Y. & Castro, A.M. & Martin, J.E. & Sinokrot, T. & Prescott, W. & Carrica, P.M., 2017. "Coupled computational fluid dynamics/multibody dynamics method for wind turbine aero-servo-elastic simulation including drivetrain dynamics," Renewable Energy, Elsevier, vol. 101(C), pages 1037-1051.
    3. Zhu, Caichao & Xu, Xiangyang & Liu, Huaiju & Luo, Tianhong & Zhai, Hongfei, 2014. "Research on dynamical characteristics of wind turbine gearboxes with flexible pins," Renewable Energy, Elsevier, vol. 68(C), pages 724-732.
    4. Mo, Shuai & Zhang, Yidu & Wu, Qiong & Matsumura, Shigeki & Houjoh, Haruo, 2016. "Load sharing behavior analysis method of wind turbine gearbox in consideration of multiple-errors," Renewable Energy, Elsevier, vol. 97(C), pages 481-491.
    5. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
    6. Helsen, Jan & Vanhollebeke, Frederik & Marrant, Ben & Vandepitte, Dirk & Desmet, Wim, 2011. "Multibody modelling of varying complexity for modal behaviour analysis of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 36(11), pages 3098-3113.
    7. Kandukuri, Surya Teja & Klausen, Andreas & Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar, 2016. "A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 697-708.
    8. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    9. Zhuang, Weichao & Zhang, Xiaowu & Ding, Yang & Wang, Liangmo & Hu, Xiaosong, 2016. "Comparison of multi-mode hybrid powertrains with multiple planetary gears," Applied Energy, Elsevier, vol. 178(C), pages 624-632.
    10. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.
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    Cited by:

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