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Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency

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  • He, Rui
  • Tian, Zhigang
  • Wang, Yifei
  • Zuo, Mingjian
  • Guo, Ziwei

Abstract

Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000820
    DOI: 10.1016/j.ress.2023.109167
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    as
    1. Francesco Papi & Lorenzo Cappugi & Simone Salvadori & Mauro Carnevale & Alessandro Bianchini, 2020. "Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines," Energies, MDPI, vol. 13(15), pages 1-18, July.
    2. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    4. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    5. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. 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.
    7. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. 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.
    10. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    11. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    12. 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.
    13. Zhang, Fengxia & Shen, Jingyuan & Liao, Haitao & Ma, Yizhong, 2021. "Optimal preventive maintenance policy for a system subject to two-phase imperfect inspections," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    14. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    15. 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.
    16. H. Cherkaoui & K.T. Huynh & A. Grall, 2018. "Quantitative assessments of performance and robustness of maintenance policies for stochastically deteriorating production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1089-1108, February.
    17. Beauson, J. & Laurent, A. & Rudolph, D.P. & Pagh Jensen, J., 2022. "The complex end-of-life of wind turbine blades: A review of the European context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    18. Walter, Gero & Flapper, Simme Douwe, 2017. "Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 227-239.
    19. Tian, Zhigang & Jin, Tongdan & Wu, Bairong & Ding, Fangfang, 2011. "Condition based maintenance optimization for wind power generation systems under continuous monitoring," Renewable Energy, Elsevier, vol. 36(5), pages 1502-1509.
    20. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    21. Wang, Yukun & Li, Xiaopeng & Chen, Junyan & Liu, Yiliu, 2022. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    22. Do Van, Phuc & Barros, Anne & Bérenguer, Christophe & Bouvard, Keomany & Brissaud, Florent, 2013. "Dynamic grouping maintenance with time limited opportunities," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 51-59.
    23. 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.
    24. Ding, Fangfang & Tian, Zhigang, 2012. "Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds," Renewable Energy, Elsevier, vol. 45(C), pages 175-182.
    25. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    26. Ma, Xiaoyang & Liu, Bin & Yang, Li & Peng, Rui & Zhang, Xiaodong, 2020. "Reliability analysis and condition-based maintenance optimization for a warm standby cooling system," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    27. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    28. Hong, H.P. & Zhou, W. & Zhang, S. & Ye, W., 2014. "Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 276-288.
    29. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    30. Waeyenbergh, Geert & Pintelon, Liliane, 2002. "A framework for maintenance concept development," International Journal of Production Economics, Elsevier, vol. 77(3), pages 299-313, June.
    31. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    32. Tian, Zhigang & Liao, Haitao, 2011. "Condition based maintenance optimization for multi-component systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 581-589.
    33. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    34. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    35. Xu, Jun & Liang, Zhenglin & Li, Yan-Fu & Wang, Kaibo, 2021. "Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
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    2. Wang, Yifei & He, Rui & Tian, Zhigang, 2023. "Opportunistic condition-based maintenance optimization for electrical distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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