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Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation

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  • Feng, Zhipeng
  • Liang, Ming
  • Zhang, Yi
  • Hou, Shumin

Abstract

Planetary gearboxes play an important role in wind turbine drive trains. Fault diagnosis of planetary gearboxes is a key topic for maintenance of wind turbines. Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature, we propose a simple yet effective method to diagnose planetary gearbox faults based on amplitude and frequency demodulations. We use the energy separation algorithm to estimate the amplitude envelope and instantaneous frequency of modulated signals for further demodulation analysis, by exploiting the adaptability of Teager energy operator to instantaneous changes in signals and the fine time resolution. However, the energy separation algorithm requires signals to be mono-components. To satisfy this requirement, we decompose signals into intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD) method as it can decompose any signal into mono-components. We further propose a criterion to guide the selection of the most relevant IMF for demodulation analysis according to the wavelet-like filter nature of EEMD and the AMFM characteristics of the planetary gearbox vibration signals. By matching the dominant peaks in the Fourier spectra of the obtained amplitude envelope and instantaneous frequency with the theoretical characteristic frequency of each gear, we can diagnose planetary gearbox faults. The principle and effectiveness of the proposed method are illustrated by simulation and the experimental analysis of signals from a planetary gearbox of a wind turbine test rig. With the proposed method, both the wear and chipping faults can be detected and located for a sun gear of the planetary gearbox test rig.

Suggested Citation

  • Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    • Handle: RePEc:eee:renene:v:47:y:2012:i:c:p:112-126
    DOI: 10.1016/j.renene.2012.04.019
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    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
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    Cited by:

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    4. Feng, Zhipeng & Qin, Sifeng & Liang, Ming, 2016. "Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions," Renewable Energy, Elsevier, vol. 85(C), pages 45-56.
    5. Hu, Aijun & Yan, Xiaoan & Xiang, Ling, 2015. "A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension," Renewable Energy, Elsevier, vol. 83(C), pages 767-778.
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    10. Feng, Zhipeng & Liang, Ming, 2014. "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis," Renewable Energy, Elsevier, vol. 66(C), pages 468-477.
    11. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    12. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    13. 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.
    14. Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.
    15. Yi, Cancan & Yu, Zhaohong & Lv, Yong & Xiao, Han, 2020. "Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis," Renewable Energy, Elsevier, vol. 161(C), pages 736-749.
    16. 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.
    17. 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.
    18. Wu, Zhe & Zhang, Qiang & Cheng, Lifeng & Hou, Shuyong & Tan, Shengyue, 2020. "The VMTES: Application to the structural health monitoring and diagnosis of rotating machines," Renewable Energy, Elsevier, vol. 162(C), pages 2380-2396.
    19. 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.
    20. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    21. Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
    22. Xiange Tian & Yongjian Jiang & Chen Liang & Cong Liu & You Ying & Hua Wang & Dahai Zhang & Peng Qian, 2022. "A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network," Energies, MDPI, vol. 15(18), pages 1-15, September.
    23. Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).

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