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Discounted cost model for condition-based maintenance optimization

Author

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  • van der Weide, J.A.M.
  • Pandey, M.D.
  • van Noortwijk, J.M.

Abstract

This paper presents methods to evaluate the reliability and optimize the maintenance of engineering systems that are damaged by shocks or transients arriving randomly in time and overall degradation is modeled as a cumulative stochastic point process. The paper presents a conceptually clear and comprehensive derivation of formulas for computing the discounted cost associated with a maintenance policy combining both condition-based and age-based criteria for preventive maintenance. The proposed discounted cost model provides a more realistic basis for optimizing the maintenance policies than those based on the asymptotic, non-discounted cost rate criterion.

Suggested Citation

  • van der Weide, J.A.M. & Pandey, M.D. & van Noortwijk, J.M., 2010. "Discounted cost model for condition-based maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 236-246.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:236-246
    DOI: 10.1016/j.ress.2009.10.004
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    References listed on IDEAS

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    1. Richard C. Morey, 1966. "Some Stochastic Properties of a Compound-Renewal Damage Model," Operations Research, INFORMS, vol. 14(5), pages 902-908, October.
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    4. Toshio Nakagawa, 2005. "Maintenance Theory of Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-221-8, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    2. Tang, Yang & Liu, Qingyou & Jing, Jiajia & Yang, Yan & Zou, Zhengwei, 2017. "A framework for identification of maintenance significant items in reliability centered maintenance," Energy, Elsevier, vol. 118(C), pages 1295-1303.
    3. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    4. Dashti, Reza & Yousefi, Shaghayegh, 2013. "Reliability based asset assessment in electrical distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 129-136.
    5. Ece Zeliha Demirci & Joachim Arts & Geert-Jan Van Houtum, 2022. "A restless bandit approach for capacitated condition based maintenance scheduling," DEM Discussion Paper Series 22-01, Department of Economics at the University of Luxembourg.
    6. Rafiee, Koosha & Feng, Qianmei & Coit, David W., 2017. "Reliability assessment of competing risks with generalized mixed shock models," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 1-11.
    7. Finkelstein, Maxim & Marais, Francois, 2010. "On terminating Poisson processes in some shock models," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 874-879.
    8. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2013. "Optimal policies for cumulative damage models with maintenance last and first," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 50-59.
    9. Vu, Hai Canh & Do, Phuc & Barros, Anne & Bérenguer, Christophe, 2014. "Maintenance grouping strategy for multi-component systems with dynamic contexts," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 233-249.
    10. Ait-Ali , Abderrahman & Odolinski, Kristofer & Pålsson, Björn & Torstensson, Peter, 2023. "Evaluating the mix of maintenance activities on railway crossings with respect to life-cycle costs," Working Papers 2023:6, Swedish National Road & Transport Research Institute (VTI).
    11. Tinga, Tiedo, 2010. "Application of physical failure models to enable usage and load based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1061-1075.
    12. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    13. van der Weide, J.A.M. & Pandey, M.D., 2011. "Stochastic analysis of shock process and modeling of condition-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 619-626.
    14. A Ponchet & M Fouladirad & A Grall, 2011. "Maintenance policy on a finite time span for a gradually deteriorating system with imperfect improvements," Journal of Risk and Reliability, , vol. 225(2), pages 105-116, June.
    15. Yang, David Y. & Frangopol, Dan M., 2019. "Life-cycle management of deteriorating civil infrastructure considering resilience to lifetime hazards: A general approach based on renewal-reward processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 197-212.
    16. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    17. Shafiee, Mahmood & Finkelstein, Maxim, 2015. "An optimal age-based group maintenance policy for multi-unit degrading systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 230-238.
    18. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
    19. Van Horenbeek, Adriaan & Van Ostaeyen, Joris & Duflou, Joost R. & Pintelon, Liliane, 2013. "Quantifying the added value of an imperfectly performing condition monitoring system—Application to a wind turbine gearbox," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 45-57.
    20. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.

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