IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3518-d381917.html
   My bibliography  Save this article

Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine

Author

Listed:
  • Jichuan Kang

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
    HEU-UL International Joint Laboratory of Naval Architecture and Offshore Technology, Harbin 150001, China)

  • Zihao Wang

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • C. Guedes Soares

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    HEU-UL International Joint Laboratory of Naval Architecture and Offshore Technology, Harbin 150001, China)

Abstract

A condition-based maintenance policy for offshore wind turbines is presented in consideration of the maintenance uncertainty and the weather effect. In this paper, the offshore wind turbine is divided into four main assemblies—namely, the rotor, gearbox, generator, and pitch system. The support vector machine classification technique is implemented to analyze the failure information, which was collected from field data in China. According to the results of fault diagnosis and prediction, the assembly that reaches the corresponding maintenance threshold will be repaired. At the same time, a maintenance opportunity occurs for the rest of the components, and an optimized plan can be determined by arranging the maintenance combination and time. The calculated results indicate that the proposed condition-based maintenance policy is beneficial to reduce the maintenance expenditure of offshore wind turbines.

Suggested Citation

  • Jichuan Kang & Zihao Wang & C. Guedes Soares, 2020. "Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine," Energies, MDPI, vol. 13(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3518-:d:381917
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3518/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/14/3518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
    2. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    3. Laura Castro-Santos & Ana Rute Bento & Carlos Guedes Soares, 2020. "The Economic Feasibility of Floating Offshore Wave Energy Farms in the North of Spain," Energies, MDPI, vol. 13(4), pages 1-19, February.
    4. Lubing Xie & Xiaoming Rui & Shuai Li & Xin Hu, 2019. "Maintenance Optimization of Offshore Wind Turbines Based on an Opportunistic Maintenance Strategy," Energies, MDPI, vol. 12(14), pages 1-26, July.
    5. Verbert, K. & De Schutter, B. & Babuška, R., 2017. "Timely condition-based maintenance planning for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 310-321.
    6. Zhou, P. & Yin, P.T., 2019. "An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 1-9.
    7. Houda Ghamlouch & Mitra Fouladirad & Antoine Grall, 2019. "The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties," Post-Print hal-02365402, HAL.
    8. Ghamlouch, Houda & Fouladirad, Mitra & Grall, Antoine, 2019. "The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 614-623.
    9. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    10. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    11. Liu, Bin & Wu, Shaomin & Xie, Min & Kuo, Way, 2017. "A condition-based maintenance policy for degrading systems with age- and state-dependent operating cost," European Journal of Operational Research, Elsevier, vol. 263(3), pages 879-887.
    12. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    13. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    2. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    3. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    4. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    5. Zhang, Shuo & Robinson, Emma & Basu, Malabika, 2022. "Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator," Renewable Energy, Elsevier, vol. 198(C), pages 936-946.
    6. Fausto Pedro García Márquez, 2022. "Special Issue on Advances in Maintenance Management," Energies, MDPI, vol. 15(7), pages 1-4, March.
    7. Nurullah Yildiz & Hassan Hemida & Charalampos Baniotopoulos, 2021. "Life Cycle Assessment of a Barge-Type Floating Wind Turbine and Comparison with Other Types of Wind Turbines," Energies, MDPI, vol. 14(18), pages 1-19, September.
    8. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

    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. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    2. Ahmed Raza & Vladimir Ulansky, 2019. "Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring," Energies, MDPI, vol. 12(19), pages 1-24, October.
    3. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    4. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Nguyen, Thi-Anh-Tuyet & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2022. "Developing an exhaustive optimal maintenance schedule for offshore wind turbines based on risk-assessment, technical factors and cost-effective evaluation," Energy, Elsevier, vol. 249(C).
    6. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Liu, Xinbao & Yang, Tianji & Pei, Jun & Liao, Haitao & Pohl, Edward A., 2019. "Replacement and inventory control for a multi-customer product service system with decreasing replacement costs," European Journal of Operational Research, Elsevier, vol. 273(2), pages 561-574.
    11. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    12. Wang, Jiantai & Longyan, Tan & Ma, Xiaobing & Gao, Kaiye & Jia, Heping & Yang, Li, 2023. "Prognosis-driven reliability analysis and replacement policy optimization for two-phase continuous degradation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Li, He & Diaz, H. & Guedes Soares, C., 2021. "A developed failure mode and effect analysis for floating offshore wind turbine support structures," Renewable Energy, Elsevier, vol. 164(C), pages 133-145.
    14. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    15. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    16. Zhang, Nailong & Si, Wujun, 2020. "Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Zheng, Rui & Zhou, Yifan, 2021. "Comparison of three preventive maintenance warranty policies for products deteriorating with age and a time-varying covariate," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    18. Saleh, Ali & Chiachío, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    19. Fallahi, F. & Bakir, I. & Yildirim, M. & Ye, Z., 2022. "A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    20. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(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:jeners:v:13:y:2020:i:14:p:3518-:d:381917. 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.