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Decision support for life extension of technical systems through virtual age modelling

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  • Pérez Ramírez, Pedro A.
  • Utne, Ingrid Bouwer

Abstract

This article presents a virtual age model for decision support regarding life extension of ageing repairable systems. The aim of the model is to evaluate different life extension decision alternatives and their impact on the future performance of the system. The model can be applied to systems operated continuously (e.g., process systems) and systems operated on demand (e.g., safety systems). Deterioration and efficiency of imperfect maintenance is assessed when there is limited or no degradation data, and only failure and maintenance data is available. Systems that are in operation can be studied, meaning that the systems may be degraded. The current degradation is represented by a “current virtual age†, which is calculated from recorded maintenance data. The model parameters are estimated with the maximum likelihood method. A case study illustrates the application of the model for life extension of two fire water pumps in an oil and gas facility. The performance of the pump system is assessed with respect to number of failures, safety unavailability and costs during the life extension period.

Suggested Citation

  • Pérez Ramírez, Pedro A. & Utne, Ingrid Bouwer, 2013. "Decision support for life extension of technical systems through virtual age modelling," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 55-69.
    • Handle: RePEc:eee:reensy:v:115:y:2013:i:c:p:55-69
    DOI: 10.1016/j.ress.2013.02.002
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Mimi & Gaudoin, Olivier & Xie, Min, 2015. "Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 245(2), pages 531-541.
    2. Toledo, Maria Luíza Guerra de & Freitas, Marta A. & Colosimo, Enrico A. & Gilardoni, Gustavo L., 2015. "ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 107-115.
    3. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
    4. Ramírez, Pedro A. Pérez & Utne, Ingrid Bouwer, 2015. "Use of dynamic Bayesian networks for life extension assessment of ageing systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 119-136.

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