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Optimum maintenance strategy under uncertainty in the lifetime distribution

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  • de Jonge, Bram
  • Klingenberg, Warse
  • Teunter, Ruud
  • Tinga, Tiedo

Abstract

The problem of determining the optimal maintenance strategy for a machine given its lifetime distribution has been studied extensively. Solutions to this problem are outlined in the academic literature, prescribed in professional handbooks, implemented in reliability engineering software systems and widely used in practice. These solutions typically assume that the lifetime distribution and its parameter values are known with certainty, although this is usually not the case in practice. In this paper we study the effect of parameter uncertainty on the optimum age-based maintenance strategy. The effect of uncertainty is evaluated by considering both a theoretical uniform lifetime distribution and a more realistic Weibull lifetime distribution. The results show that admitting to the uncertainty does influence the optimal maintenance age and also provides a quantifiable cost benefit. The results can help maintenance managers in making maintenance decisions under uncertainty, and also in deciding when it is worthwhile to invest in advanced data improvement procedures.

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  • 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.
    • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:59-67
    DOI: 10.1016/j.ress.2014.09.013
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    2. 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.
    3. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2023. "A closed-loop maintenance strategy for offshore wind farms: Incorporating dynamic wind farm states and uncertainty-awareness in decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    4. Fouladirad, Mitra & Paroissin, Christian & Grall, Antoine, 2018. "Sensitivity of optimal replacement policies to lifetime parameter estimates," European Journal of Operational Research, Elsevier, vol. 266(3), pages 963-975.
    5. Olde Keizer, Minou C.A. & Teunter, Ruud H. & Veldman, Jasper, 2016. "Clustering condition-based maintenance for systems with redundancy and economic dependencies," European Journal of Operational Research, Elsevier, vol. 251(2), pages 531-540.
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    9. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. 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.
    11. 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.
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    14. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    15. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.
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    17. Radim Briš & Nuong Thi Thuy Tran, 2023. "Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

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