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Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Author

Listed:
  • Lixiao Cao

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China)

  • Zheng Qian

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China)

  • Hamid Zareipour

    (Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

  • David Wood

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

  • Ehsan Mollasalehi

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada)

  • Shuangshu Tian

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China)

  • Yan Pei

    (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China)

Abstract

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3318-:d:186069
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    References listed on IDEAS

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    Cited by:

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    2. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
    3. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    4. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    5. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    6. T. M. I. Mahlia & H. Syaheed & A. E. Pg Abas & F. Kusumo & A. H. Shamsuddin & Hwai Chyuan Ong & M. R. Bilad, 2019. "Organic Rankine Cycle (ORC) System Applications for Solar Energy: Recent Technological Advances," Energies, MDPI, vol. 12(15), pages 1-19, July.

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