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Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis

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  • Feng, Zhipeng
  • Liang, Ming

Abstract

Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox).

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  • 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.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:468-477
    DOI: 10.1016/j.renene.2013.12.047
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    1. 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.
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    1. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.
    2. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    3. de Bessa, Iury Valente & Palhares, Reinaldo Martinez & D'Angelo, Marcos Flávio Silveira Vasconcelos & Chaves Filho, João Edgar, 2016. "Data-driven fault detection and isolation scheme for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 87(P1), pages 634-645.
    4. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
    5. 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.
    6. 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.
    7. 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.
    8. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    9. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
    10. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
    11. 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.
    12. 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.
    13. Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.
    14. Cho, Seongpil & Gao, Zhen & Moan, Torgeir, 2018. "Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 306-321.
    15. 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.
    16. Alvarez, Eduardo J. & Ribaric, Adrijan P., 2018. "An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA," Renewable Energy, Elsevier, vol. 115(C), pages 391-399.

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