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Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Author

Listed:
  • Jannie S. Nielsen

    (Department of Civil Engineering, Aalborg University, Thomas Manns vej 23, DK-9220 Aalborg East, Denmark)

  • John D. Sørensen

    (Department of Civil Engineering, Aalborg University, Thomas Manns vej 23, DK-9220 Aalborg East, Denmark)

Abstract

To optimally plan maintenance of wind turbine blades, knowledge of the degradation processes and the remaining useful life is essential. In this paper, a method is proposed for calibration of a Markov deterioration model based on past inspection data for a range of blades, and updating of the model for a specific wind turbine blade, whenever information is available from inspections and/or condition monitoring. Dynamic Bayesian networks are used to obtain probabilities of inspection outcomes for a maximum likelihood estimation of the transition probabilities in the Markov model, and are used again when updating the model for a specific blade using observations. The method is illustrated using indicative data from a database containing data from inspections of wind turbine blades.

Suggested Citation

  • Jannie S. Nielsen & John D. Sørensen, 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades," Energies, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:664-:d:98092
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    References listed on IDEAS

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    1. Jannie Sønderkær Nielsen & John Dalsgaard Sørensen, 2014. "Methods for Risk-Based Planning of O&M of Wind Turbines," Energies, MDPI, vol. 7(10), pages 1-20, October.
    2. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
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    Cited by:

    1. Jyh-Cherng Gu & Chun-Hung Liu & Kai-Ying Chou & Ming-Ta Yang, 2019. "Research on CBM of the Intelligent Substation SCADA System," Energies, MDPI, vol. 12(20), pages 1-22, October.
    2. 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).
    3. Jannie Sønderkær Nielsen & Lindsay Miller-Branovacki & Rupp Carriveau, 2021. "Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components," Energies, MDPI, vol. 14(4), pages 1-16, February.
    4. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    5. Antoine Chrétien & Antoine Tahan & Philippe Cambron & Adaiton Oliveira-Filho, 2023. "Operational Wind Turbine Blade Damage Evaluation Based on 10-min SCADA and 1 Hz Data," Energies, MDPI, vol. 16(7), pages 1-18, March.
    6. Alfredo Alcayde & Quetzalcoatl Hernandez-Escobedo & David Muñoz-Rodríguez & Alberto-Jesus Perea-Moreno, 2022. "Worldwide Research Trends on Optimizing Wind Turbine Efficiency," Energies, MDPI, vol. 15(18), pages 1-7, September.
    7. Xiyun Yang & Guo Fu & Yanfeng Zhang & Ning Kang & Feng Gao, 2017. "A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization," Energies, MDPI, vol. 10(11), pages 1-15, November.
    8. Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    10. 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.
    11. Mikkel Schou Nielsen & Ivan Nikolov & Emil Krog Kruse & Jørgen Garnæs & Claus Brøndgaard Madsen, 2020. "High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness," Energies, MDPI, vol. 13(15), pages 1-17, July.
    12. Verma, Amrit Shankar & Yan, Jiquan & Hu, Weifei & Jiang, Zhiyu & Shi, Wei & Teuwen, Julie J.E., 2023. "A review of impact loads on composite wind turbine blades: Impact threats and classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    13. Antoine Chrétien & Antoine Tahan & Francis Pelletier, 2024. "Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data," Energies, MDPI, vol. 17(5), pages 1-21, March.
    14. Patrick D. Moroney & Amrit Shankar Verma, 2023. "Durability and Damage Tolerance Analysis Approaches for Wind Turbine Blade Trailing Edge Life Prediction: A Technical Review," Energies, MDPI, vol. 16(24), pages 1-33, December.
    15. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    16. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    17. 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.
    18. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    19. Dawid Augustyn & Martin D. Ulriksen & John D. Sørensen, 2021. "Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information," Energies, MDPI, vol. 14(18), pages 1-23, September.

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