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Timely condition-based maintenance planning for multi-component systems

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  • Verbert, K.
  • De Schutter, B.
  • BabuÅ¡ka, R.

Abstract

Last-minute maintenance planning is often undesirable, as it may cause downtime during operational hours, may require rescheduling of other activities, and does not allow to optimize the management of spare parts, material, and personnel. In spite of the aforementioned drawbacks of last-minute planning, most existing methods plan maintenance activities at the last minute. In this paper, we propose a new strategy for timely maintenance planning in multi-component systems. As a first step, we determine for each system component independently the most appropriate maintenance planning strategy. This way, the maintenance decisions can be tailored to the specific situations. For example, conservative maintenance decisions can be taken when the risk tolerance is low, and maintenance decisions can be made timely when we can accurately predict future degradation behavior. In the second step, we optimize the maintenance plan at the system level. Here, we account for economic and structural dependence with the aim to profit from spreading or combining various maintenance activities. The applicability of the method is demonstrated on a railway case. It is shown how the different cost functions (e.g. costs of maintenance, downtime, and failure) influence the maintenance decisions.

Suggested Citation

  • Verbert, K. & De Schutter, B. & BabuÅ¡ka, R., 2017. "Timely condition-based maintenance planning for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 310-321.
  • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:310-321
    DOI: 10.1016/j.ress.2016.10.032
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    8. Carlos Oliveira Cruz & Joaquim Miranda Sarmento, 2018. "Maximizing the value for money of road projects through digitalization," Competition and Regulation in Network Industries, , vol. 19(1-2), pages 69-92, March.
    9. McMorland, Jade & Flannigan, Callum & Carroll, James & Collu, Maurizio & McMillan, David & Leithead, William & Coraddu, Andrea, 2022. "A review of operations and maintenance modelling with considerations for novel wind turbine concepts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    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. Liu, Xinbao & Yang, Tianji & Pei, Jun & Liao, Haitao & Pohl, Edward A., 2019. "Replacement and inventory control for a multi-customer product service system with decreasing replacement costs," European Journal of Operational Research, Elsevier, vol. 273(2), pages 561-574.
    12. Liu, Bin & Pandey, Mahesh D. & Wang, Xiaolin & Zhao, Xiujie, 2021. "A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes," European Journal of Operational Research, Elsevier, vol. 295(2), pages 705-717.
    13. Huynh, K.T. & Vu, H.C. & Nguyen, T.D. & Ho, A.C., 2022. "A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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