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A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique

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  • He, Guolin
  • Ding, Kang
  • Li, Weihua
  • Jiao, Xintao

Abstract

Wind turbine gearboxes generally exhibit complex vibration characteristics due to wide variations in the operating conditions, and dynamics of the structure coupled with flexible supports. Conventional spectral analysis method may not provide reliable health monitoring and fault diagnosis of the gearbox. In this study, a novel order tracking method based on discrete spectrum correction technique is proposed to analyze wind turbine gearbox vibration for the purposes of health monitoring and fault diagnosis. The effectiveness and robustness of the proposed method are demonstrated through simulations and engineering tests. The results show that the shaft rotating speed could be accurately identified from the vibration signal together with amplitudes of significant gear meshing components. Modulation sidebands of both the planetary and fixed-shaft gears in a healthy wind turbine gearbox were further analyzed, which revealed inherent shaft misalignment in the fixed-shaft gear. Meshing frequency of the planetary gear was modulated by both the rotating frequencies of sun gear and planetary carrier, while fundamental modulation frequency of the planetary carrier was found to be related to rotating frequency of the carrier multiplied by the number of planet gears. The monitoring of such particular vibration features would be helpful in enhancing the operational performance of wind turbines through reliable health monitoring of gearboxes and reducing the misdiagnosis of faults.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:364-375
    DOI: 10.1016/j.renene.2015.10.036
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    References listed on IDEAS

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    1. Jiang, Yonghua & Tang, Baoping & Qin, Yi & Liu, Wenyi, 2011. "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD," Renewable Energy, Elsevier, vol. 36(8), pages 2146-2153.
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    5. 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.
    6. 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.
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    Cited by:

    1. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
    2. Yaoming Li & Yanbin Liu & Kuizhou Ji & Ruiheng Zhu, 2022. "A Fault Diagnosis Method for a Differential Inverse Gearbox of a Crawler Combine Harvester Based on Order Analysis," Agriculture, MDPI, vol. 12(9), pages 1-14, August.
    3. Pan, Yubin & Hong, Rongjing & Chen, Jie & Wu, Weiwei, 2020. "A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox," Renewable Energy, Elsevier, vol. 152(C), pages 138-154.
    4. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    5. Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.

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