IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v231y2017i2p121-129.html
   My bibliography  Save this article

Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model

Author

Listed:
  • Peyman Mazidi
  • Lina Bertling Tjernberg
  • Miguel A Sanz Bobi

Abstract

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.

Suggested Citation

  • Peyman Mazidi & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model," Journal of Risk and Reliability, , vol. 231(2), pages 121-129, April.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:2:p:121-129
    DOI: 10.1177/1748006X16686899
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X16686899
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X16686899?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    2. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    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. Samet Ozturk & Vasilis Fthenakis & Stefan Faulstich, 2018. "Assessing the Factors Impacting on the Reliability of Wind Turbines via Survival Analysis—A Case Study," Energies, MDPI, vol. 11(11), pages 1-20, November.
    2. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    3. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    4. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    5. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    6. Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.

    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. 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).
    2. 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).
    3. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.
    4. Xu, Xuefang & Li, Bo & Qiao, Zijian & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong, 2023. "Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain," Renewable Energy, Elsevier, vol. 219(P1).
    5. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    6. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    7. Jäger-Roschko, Moritz & Weigell, Jürgen & Jahn, Carlos, 2019. "Modelling of spare parts storage strategies for offshore wind," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 83-108, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. 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).
    9. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    10. 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).
    11. Augusto Bianchini & Jessica Rossi & Lauro Antipodi, 2018. "A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 999-1013, October.
    12. 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.
    13. Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.
    14. Xia, Tangbin & Dong, Yifan & Pan, Ershun & Zheng, Meimei & Wang, Hao & Xi, Lifeng, 2021. "Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating," Renewable Energy, Elsevier, vol. 163(C), pages 1444-1454.
    15. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    16. 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.
    17. Mahmood Shafiee & Maxim Finkelstein, 2015. "A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines," Journal of Risk and Reliability, , vol. 229(5), pages 373-384, October.
    18. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2019. "Improved maintenance optimization of offshore wind systems considering effects of government subsidies, lost production and discounted cost model," Energy, Elsevier, vol. 187(C).
    19. Peyman Mazidi & Mian Du & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "A health condition model for wind turbine monitoring through neural networks and proportional hazard models," Journal of Risk and Reliability, , vol. 231(5), pages 481-494, October.
    20. Yang Lu & Liping Sun & Yanzhuo Xue, 2021. "Research on a Comprehensive Maintenance Optimization Strategy for an Offshore Wind Farm," Energies, MDPI, vol. 14(4), pages 1-22, February.

    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:sae:risrel:v:231:y:2017:i:2:p:121-129. 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: SAGE Publications (email available below). General contact details of provider: .

    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.