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Impact of few failure data on the opportunistic replacement policy for multi-component systems

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  • Laggoune, Radouane
  • Chateauneuf, Alaa
  • Aissani, Djamil

Abstract

In continuous operating units, the production loss is often very large during the system shut down. Their economic profitability is conditioned by the implementation of suitable maintenance policy that could increase the availability and reduce the operating costs. In this paper, an opportunistic replacement policy is proposed for multi-component series system in the context of data uncertainty, where the expected total cost per unit time is minimized under general lifetime distribution. When the system is down, either correctively or preventively, the opportunity to replace preventively non-failed components is considered. To deal with the problem of the small size of failure data samples, the Bootstrap technique is applied, in order to model the uncertainties in parameter estimates. The Weibull parameters are considered as random variables rather than just deterministic point estimates. A solution procedure based on Monte Carlo simulations with informative search method is proposed and applied to the optimization of preventive maintenance plan for a hydrogen compressor in an oil refinery.

Suggested Citation

  • Laggoune, Radouane & Chateauneuf, Alaa & Aissani, Djamil, 2010. "Impact of few failure data on the opportunistic replacement policy for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 108-119.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:2:p:108-119
    DOI: 10.1016/j.ress.2009.08.007
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    References listed on IDEAS

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    Cited by:

    1. de Jonge, Bram & Dijkstra, Arjan S. & Romeijnders, Ward, 2015. "Cost benefits of postponing time-based maintenance under lifetime distribution uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 15-21.
    2. 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.
    3. Aseem K. Mishra & Divya Shrivastava & Prem Vrat, 2020. "An opportunistic group maintenance model for the multi-unit series system employing Jaya algorithm," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 603-628, June.
    4. Hu, Jiawen & Shen, Jingyuan & Shen, Lijuan, 2020. "Opportunistic maintenance for two-component series systems subject to dependent degradation and shock," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Berrade, M.D. & Scarf, P.A. & Cavalcante, C.A.V., 2017. "A study of postponed replacement in a delay time model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 70-79.
    6. Sánchez-Herguedas, Antonio & Mena-Nieto, Angel & Rodrigo-Muñoz, Francisco, 2021. "A new analytical method to optimise the preventive maintenance interval by using a semi-Markov process and z-transform with an application to marine diesel engines," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    7. Rokstad, Marius Møller & Ugarelli, Rita Maria, 2015. "Minimising the total cost of renewal and risk of water infrastructure assets by grouping renewal interventions," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 148-160.
    8. El Hassene Ait Mokhtar & Radouane Laggoune & Alaa Chateauneuf, 2017. "Benefit and customer demand approach for maintenance optimization of complex systems using Bayesian networks," Journal of Risk and Reliability, , vol. 231(5), pages 558-572, October.
    9. Cavalcante, Cristiano A.V. & Lopes, Rodrigo S. & Scarf, Philip A., 2021. "Inspection and replacement policy with a fixed periodic schedule," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    10. Majdi Argoubi & Emna Ammari & Hatem Masri, 2021. "A scientometric analysis of Operations Research and Management Science research in Africa," Operational Research, Springer, vol. 21(3), pages 1827-1843, September.
    11. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Age-based maintenance under population heterogeneity: Optimal exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1007-1020.
    12. Zhang, Xiaohong & Zeng, Jianchao, 2015. "A general modeling method for opportunistic maintenance modeling of multi-unit systems," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 176-190.
    13. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    14. Cavalcante, C.A.V. & Lopes, R.S. & Scarf, P.A., 2018. "A general inspection and opportunistic replacement policy for one-component systems of variable quality," European Journal of Operational Research, Elsevier, vol. 266(3), pages 911-919.
    15. Scarf, P.A. & Cavalcante, C.A.V. & Lopes, R.S., 2019. "Delay-time modelling of a critical system subject to random inspections," European Journal of Operational Research, Elsevier, vol. 278(3), pages 772-782.
    16. de Jonge, Bram & Klingenberg, Warse & Teunter, Ruud & Tinga, Tiedo, 2015. "Optimum maintenance strategy under uncertainty in the lifetime distribution," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 59-67.
    17. Oakley, Jordan L. & Wilson, Kevin J. & Philipson, Pete, 2022. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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