IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v226y2012i2p221-233.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X11421265
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X11421265?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Finkelstein, Maxim & Cha, Ji Hwan & Bedford, Tim, 2023. "Optimal preventive maintenance strategy for populations of systems that generate outputs," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Hashemi, M. & Asadi, M. & Zarezadeh, S., 2020. "Optimal maintenance policies for coherent systems with multi-type components," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    3. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Ji Hwan Cha & Maxim Finkelstein, 2020. "On optimal life extension for degrading systems," Journal of Risk and Reliability, , vol. 234(3), pages 487-495, June.
    5. Belyi, Dmitriy & Popova, Elmira & Morton, David P. & Damien, Paul, 2017. "Bayesian failure-rate modeling and preventive maintenance optimization," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1085-1093.
    6. Ming-Yi You & Guang Meng, 2012. "A modularized framework for predictive maintenance scheduling," Journal of Risk and Reliability, , vol. 226(4), pages 380-391, August.
    7. 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.
    8. Si, Xiao-Sheng & Chen, Mao-Yin & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "Specifying measurement errors for required lifetime estimation performance," European Journal of Operational Research, Elsevier, vol. 231(3), pages 631-644.
    9. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    10. Lin, Zu-Liang & Huang, Yeu-Shiang & Fang, Chih-Chiang, 2015. "Non-periodic preventive maintenance with reliability thresholds for complex repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 145-156.
    11. Wang, Naichao & Li, Mingyuan & Xiao, Boping & Ma, Lin, 2019. "Availability analysis of a general time distribution system with the consideration of maintenance and spares," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    12. Badía, F.G. & Berrade, M.D. & Cha, Ji Hwan & Lee, Hyunju, 2018. "Optimal replacement policy under a general failure and repair model: Minimal versus worse than old repair," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 362-372.
    13. Zhang, Qin & Fang, Zhigeng & Cai, Jiajia, 2021. "Preventive replacement policies with multiple missions and maintenance triggering approaches," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    14. Poppe, Joeri & Basten, Rob J.I. & Boute, Robert N. & Lambrecht, Marc R., 2017. "Numerical study of inventory management under various maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 262-273.
    15. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    16. Hamidi, Maryam & Szidarovszky, Ferenc & Szidarovszky, Miklos, 2016. "New one cycle criteria for optimizing preventive replacement policies," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 42-48.
    17. Sheu, Shey-Huei & Tsai, Hsin-Nan & Sheu, Uan-Yu & Zhang, Zhe George, 2019. "Optimal replacement policies for a system based on a one-cycle criterion," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    18. 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.
    19. Liu, Peng & Wang, Guanjun, 2022. "Minimal repair models with non-negligible repair time," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    20. Sharafali, Moosa & Tarakci, Hakan & Kulkarni, Shailesh & Razack Shahul Hameed, Raja Abdul, 2019. "Optimal delivery due date for a supplier with an unreliable machine under outsourced maintenance," International Journal of Production Economics, Elsevier, vol. 208(C), pages 53-68.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:226:y:2012:i:2:p:221-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.