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The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties

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  • Houda Ghamlouch

    (LM2S - Laboratoire Modélisation et Sûreté des Systèmes - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique)

  • Mitra Fouladirad

    (LM2S - Laboratoire Modélisation et Sûreté des Systèmes - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique)

  • Antoine Grall

    (LM2S - Laboratoire Modélisation et Sûreté des Systèmes - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique)

Abstract

Preventive maintenance planning is an important problem for the handling of energy production systems with high down time costs. Throughout the last decade different maintenance strategies have been developed and optimized in order to minimize operational and maintenance costs whilst conserving and improving the system reliability and productivity. Preventive maintenance strategies are usually based on the monitoring and the prediction of the system behavior and its deterioration process. However, some industrial systems may be operating under a dynamic environment and/or variable working conditions. In this case both the deterioration and the production processes may not be deterministic and incorporate different types of uncertainties. In this paper, we consider the case of a preventive maintenance strategy for a production system subject to uncertainty. For this system, a decision-making procedure for condition-based maintenance planning is proposed. In order to consider uncertainty in production and deterioration processes, these latter are modeled by non-monotonic stochastic processes. The modeling of deterioration processes by means of jump-diffusion stochastic processes has been proposed in our previous work. In this paper, a decision-making approach for preventive maintenance strategies is proposed. Knowing the remaining useful life of a system, a simulation-based real options analysis is used in order to determine the best date to maintain. Considering a case study of a wind turbine with PHM structure, the decision-making approach is described and tested through an empirical example.

Suggested Citation

  • 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.
    • Handle: RePEc:hal:journl:hal-02365402
    DOI: 10.1016/j.ress.2017.10.001
    Note: View the original document on HAL open archive server: https://utt.hal.science/hal-02365402
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    1. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    2. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    3. Widén, Joakim & Wäckelgård, Ewa, 2010. "A high-resolution stochastic model of domestic activity patterns and electricity demand," Applied Energy, Elsevier, vol. 87(6), pages 1880-1892, June.
    4. S. G. Kou & Hui Wang, 2004. "Option Pricing Under a Double Exponential Jump Diffusion Model," Management Science, INFORMS, vol. 50(9), pages 1178-1192, September.
    5. van Donselaar, K. & van den Nieuwenhof, J. & Visschers, J., 2000. "The impact of material coordination concepts on planning stability in supply chains," International Journal of Production Economics, Elsevier, vol. 68(2), pages 169-176, November.
    6. Pritchard, Geoffrey, 2015. "Stochastic inflow modeling for hydropower scheduling problems," European Journal of Operational Research, Elsevier, vol. 246(2), pages 496-504.
    7. van Noortwijk, J.M. & van der Weide, J.A.M. & Kallen, M.J. & Pandey, M.D., 2007. "Gamma processes and peaks-over-threshold distributions for time-dependent reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1651-1658.
    8. Jin, Xiaoning & Li, Lin & Ni, Jun, 2009. "Option model for joint production and preventive maintenance system," International Journal of Production Economics, Elsevier, vol. 119(2), pages 347-353, June.
    9. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    10. Stephen C. Graves, 2011. "Uncertainty and Production Planning," International Series in Operations Research & Management Science, in: Karl G. Kempf & Pınar Keskinocak & Reha Uzsoy (ed.), Planning Production and Inventories in the Extended Enterprise, chapter 0, pages 83-101, Springer.
    11. Suleyman Karabuk & S. David Wu, 2003. "Coordinating Strategic Capacity Planning in the Semiconductor Industry," Operations Research, INFORMS, vol. 51(6), pages 839-849, December.
    12. Zhi‐Sheng Ye & Min Xie, 2015. "Stochastic modelling and analysis of degradation for highly reliable products," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 16-32, January.
    13. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    14. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
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    Cited by:

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    2. Azimpoor, Samareh & Taghipour, Sharareh & Farmanesh, Babak & Sharifi, Mani, 2022. "Joint Planning of Production and Inspection of Parallel Machines with Two-phase of Failure," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. 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).
    4. 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).
    5. Yeter, B. & Garbatov, Y. & Guedes Soares, C., 2022. "Life-extension classification of offshore wind assets using unsupervised machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    10. 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.
    11. Mizutani, Daijiro & Nakazato, Yuto & Ikushima, Rie & Satsukawa, Koki & Kawasaki, Yosuke & Kuwahara, Masao, 2024. "Optimal intervention policy of emergency storage batteries for expressway transportation systems considering deterioration risk during lead time of replacement," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. 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.
    13. Chen, Zhiyuan & Wang, Feng & Wang, Tieli & He, Rulin & Hu, Jieli & Li, Li & Luo, Ying & Qin, Yingling & Wang, Dingliang, 2024. "A real options approach to renewable energy module end-of-life decisions under multiple uncertainties: Application to PV and wind in China," Renewable Energy, Elsevier, vol. 226(C).

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