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Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Author

Listed:
  • Zhenen Li

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Xinyan Zhang

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Tusongjiang Kari

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Wei Hu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

Abstract

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

Suggested Citation

  • Zhenen Li & Xinyan Zhang & Tusongjiang Kari & Wei Hu, 2021. "Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings," Energies, MDPI, vol. 14(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4612-:d:604835
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    References listed on IDEAS

    as
    1. Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin, 2021. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Tarek Berghout & Mohamed Benbouzid & Leïla-Hayet Mouss, 2021. "Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
    3. Benedikt Wiese & Niels L. Pedersen & Esmaeil S. Nadimi & Jürgen Herp, 2020. "Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures," Energies, MDPI, vol. 13(13), pages 1-11, July.
    4. Shijun Xu & Yi Hou & Xinpu Deng & Kewei Ouyang & Ye Zhang & Shilin Zhou, 2021. "Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance," Energies, MDPI, vol. 14(4), pages 1-25, February.
    5. Si, Xiao-Sheng & Wang, Wenbin & Chen, Mao-Yin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution," European Journal of Operational Research, Elsevier, vol. 226(1), pages 53-66.
    6. Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin & Qiang Wang & Ying Zhu & Xiaowen Pu, 2021. "A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, February.
    7. Hui Li & Fan Li & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2021. "Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework," Energies, MDPI, vol. 14(6), pages 1-19, March.
    8. Martinez-Luengo, Maria & Kolios, Athanasios & Wang, Lin, 2016. "Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 91-105.
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