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A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process

Author

Listed:
  • Hu, Yaogang
  • Li, Hui
  • Shi, Pingping
  • Chai, Zhaosen
  • Wang, Kun
  • Xie, Xiangjie
  • Chen, Zhe

Abstract

A performance degradation model and a real-time remaining useful life (RUL) prediction method are proposed on the basis of temperature characteristic parameters to determine the RUL of wind turbine bearings. First, using the moving average method, the relative temperature data of wind turbine bearings are smoothed, and the temperature trend data are obtained on the basis of the uncertainty of wind speed and wind direction that causes the temperature of wind turbine bearings to vary widely. Second, given that the degradation speed of bearings changes with operational time and uncertain external factors, the performance degradation model is established with the Wiener process. The parameters of this model are obtained through the maximum likelihood estimation method. Third, according to the failure principle of the first temperature monitoring value beyond the first warning threshold, the RUL prediction model for wind turbine bearings is established on the basis of an inverse Gaussian distribution. Finally, the performance degradation process and real-time RUL prediction are demonstrated by predicting the RUL of a practical rear bearing of a wind turbine generator. The comparison of the predicted RUL and actual RUL shows that the proposed model and prediction method are correct and effective.

Suggested Citation

  • Hu, Yaogang & Li, Hui & Shi, Pingping & Chai, Zhaosen & Wang, Kun & Xie, Xiangjie & Chen, Zhe, 2018. "A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process," Renewable Energy, Elsevier, vol. 127(C), pages 452-460.
  • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:452-460
    DOI: 10.1016/j.renene.2018.04.033
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    References listed on IDEAS

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    1. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    2. Yang, Dong & Li, Hui & Hu, Yaogang & Zhao, Jie & Xiao, Hongwei & Lan, Yongsen, 2016. "Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion," Renewable Energy, Elsevier, vol. 92(C), pages 104-116.
    3. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
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    5. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
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    Cited by:

    1. Zheng Wang & Peng Gao & Xuening Chu, 2022. "Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    2. Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.
    3. Rommel, D.P. & Di Maio, D. & Tinga, T., 2020. "Calculating wind turbine component loads for improved life prediction," Renewable Energy, Elsevier, vol. 146(C), pages 223-241.
    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. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    6. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    7. Jin Cui & Licai Cao & Tianxiao Zhang, 2024. "A two-stage Gaussian process regression model for remaining useful prediction of bearings," Journal of Risk and Reliability, , vol. 238(2), pages 333-348, April.
    8. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. 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.
    10. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    11. Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).

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