IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3802-d1073813.html
   My bibliography  Save this article

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Author

Listed:
  • Xiangfu Zou

    (College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
    Hunan Sunward Science and Technologies Co., Ltd., Zhuzhou 412000, China)

  • Jie Zhang

    (Wind Power Division, Zhuzhou Electric Locomotive Research Institute Co., Zhouzhou 412000, China)

  • Jian Chen

    (College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China)

  • Ognjen Orozovic

    (School of Engineering, The University of Newcastle, Callaghan 2308, Australia)

  • Xihua Xie

    (College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China)

  • Jiejie Li

    (College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China)

Abstract

Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3802-:d:1073813
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3802/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3802/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ding, Fangfang & Tian, Zhigang & Zhao, Fuqiong & Xu, Hao, 2018. "An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously varying load conditions," Renewable Energy, Elsevier, vol. 129(PA), pages 260-270.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    2. W. Dheelibun Remigius & Anand Natarajan, 2022. "A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(1), January.
    3. Eric Lucas dos Santos Cabral & Mario Orestes Aguirre Gonzalez & Priscila da Cunha Jacome Vidal & Joao Florencio da Costa Junior & Rafael Monteiro de Vasconcelos & David Cassimiro de Melo & Ruan Lucas , 2024. "Optimization Models for Operations and Maintenance of Offshore Wind Turbines Based on Artificial Intelligence and Operations Research: A Systematic Literature Review," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(3), pages 1-1, June.
    4. 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.
    5. Abdulrahman A. Alghamdi & Abdelhameed Ibrahim & El-Sayed M. El-Kenawy & Abdelaziz A. Abdelhamid, 2023. "Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-30, January.
    6. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    8. Wang, Jingjing & Zhao, Xian & Guo, Xiaoxin, 2019. "Optimizing wind turbine's maintenance policies under performance-based contract," Renewable Energy, Elsevier, vol. 135(C), pages 626-634.
    9. Liao, Ding & Zhu, Shun-Peng & Correia, José A.F.O. & De Jesus, Abílio M.P. & Veljkovic, Milan & Berto, Filippo, 2022. "Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects," Renewable Energy, Elsevier, vol. 200(C), pages 724-742.
    10. Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
    11. Ming Li & Yuan Luo & Liyang Xie & Cao Tong & Chuan Chen, 2024. "Fatigue reliability assessment method for wind power gear system based on multidimensional finite element method," Journal of Risk and Reliability, , vol. 238(3), pages 540-558, June.
    12. 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.
    13. 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.
    14. Verstraeten, Timothy & Nowé, Ann & Keller, Jonathan & Guo, Yi & Sheng, Shuangwen & Helsen, Jan, 2019. "Fleetwide data-enabled reliability improvement of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 428-437.
    15. Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3802-:d:1073813. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.