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Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

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  • Wei Teng

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
    Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Beijing 102206, China)

  • Xiaolong Zhang

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Yibing Liu

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
    Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Beijing 102206, China)

  • Andrew Kusiak

    (Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA)

  • Zhiyong Ma

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN) is used to train data-driven models and to predict short-term tendencies of feature series. By combining the predicted and training features, a polynomial curve reflecting the long-term degradation process of bearings is fitted. Through solving the intersection between the fitted curve and the pre-defined threshold, the RUL can be deduced. The presented approach is validated by an operating wind turbine with a faulty bearing in the gearbox.

Suggested Citation

  • Wei Teng & Xiaolong Zhang & Yibing Liu & Andrew Kusiak & Zhiyong Ma, 2016. "Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox," Energies, MDPI, vol. 10(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:10:y:2016:i:1:p:32-:d:86638
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    References listed on IDEAS

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    Cited by:

    1. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    2. Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
    3. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    4. Xiangfu Zou & Jie Zhang & Jian Chen & Ognjen Orozovic & Xihua Xie & Jiejie Li, 2023. "Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    5. 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.

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