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A condition- and age-based replacement model using delay time modelling

Author

Listed:
  • T F Lipi
  • J-H Lim
  • M J Zuo
  • W Wang

Abstract

To make realistic maintenance decisions, it is important that maintenance managers make their preventive replacement decisions based on observations of the condition of their equipment. This study addresses a condition- and age-based replacement decision problem using the complete history of measured condition observations to minimize long-run average cost, maximize long-run average availability, or both. A stochastic filtering process (SFP) is used to estimate the residual lifetime distribution conditional on the history of observed condition information. A long-run average cost model and a long-run average availability model are analysed in order to determine the theorems necessary for calculating the optimum replacement time. To minimize the cost and maximize availability, a multiobjective decision frontier is proposed that will help maintenance managers deal with trade-offs between the two objectives. Finally, numerical examples are presented for each scenario to show the effectiveness of the methods proposed.

Suggested Citation

  • T F Lipi & J-H Lim & M J Zuo & W Wang, 2012. "A condition- and age-based replacement model using delay time modelling," Journal of Risk and Reliability, , vol. 226(2), pages 221-233, April.
    • Handle: RePEc:sae:risrel:v:226:y:2012:i:2:p:221-233
    DOI: 10.1177/1748006X11421265
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    References listed on IDEAS

    as
    1. Coolen-Schrijner, P. & Coolen, F.P.A., 2007. "Nonparametric adaptive age replacement with a one-cycle criterion," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 74-84.
    2. de Castro, Hélio Fiori & Cavalca, Katia Lucchesi, 2006. "Maintenance resources optimization applied to a manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 413-420.
    3. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    4. Richard Barlow & Larry Hunter, 1960. "Optimum Preventive Maintenance Policies," Operations Research, INFORMS, vol. 8(1), pages 90-100, February.
    5. B. Castanier & C. Bérenguer & A. Grall, 2003. "A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(4), pages 327-347, October.
    6. Savsar, Mehmet, 2006. "Effects of maintenance policies on the productivity of flexible manufacturing cells," Omega, Elsevier, vol. 34(3), pages 274-282, June.
    7. P Coolen-Schrijner & F P A Coolen & S C Shaw, 2006. "Nonparametric adaptive opportunity-based age replacement strategies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 63-81, January.
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