IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v130y2014icp33-41.html
   My bibliography  Save this article

Cost modelling in maintenance strategy optimisation for infrastructure assets with limited data

Author

Listed:
  • Zhang, Wenjuan
  • Wang, Wenbin

Abstract

Our paper reports on the use of cost modelling in maintenance strategy optimisation for infrastructure assets. We present an original approach: the possibility of modelling even when the data and information usually required are not sufficient in quantity and quality. Our method makes use of subjective expert knowledge, and requires information gathered for only a small sample of assets to start with. Bayes linear methods are adopted to combine the subjective expert knowledge with the sample data to estimate the unknown model parameters of the cost model. When new information becomes available, Bayes linear methods also prove useful in updating these estimates. We use a case study from the rail industry to demonstrate our methods. The optimal maintenance strategy is obtained via simulation based on the estimated model parameters and the strategy with the least unit time cost is identified. When the optimal strategy is not followed due to insufficient funding, the future costs of recovering the degraded asset condition are estimated.

Suggested Citation

  • Zhang, Wenjuan & Wang, Wenbin, 2014. "Cost modelling in maintenance strategy optimisation for infrastructure assets with limited data," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 33-41.
    • Handle: RePEc:eee:reensy:v:130:y:2014:i:c:p:33-41
    DOI: 10.1016/j.ress.2014.04.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832014000933
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2014.04.025?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. O'Hagan & E. B. Glennie & R. E. Beardsall, 1992. "Subjective Modelling and Bayes Linear Estimation in the Uk Water Industry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(3), pages 563-577, November.
    2. Wang, W. & Zhang, W., 2008. "An asset residual life prediction model based on expert judgments," European Journal of Operational Research, Elsevier, vol. 188(2), pages 496-505, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nafisah, Ibrahim & Shrahili, Mansour & Alotaibi, Naif & Scarf, Phil, 2019. "Virtual series-system models of imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 604-613.
    2. Sasidharan, M. & Burrow, M.P.N. & Ghataora, G.S., 2020. "A whole life cycle approach under uncertainty for economically justifiable ballasted railway track maintenance," Research in Transportation Economics, Elsevier, vol. 80(C).

    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. Mario P. Brito & Ian G. J. Dawson, 2020. "Predicting the Validity of Expert Judgments in Assessing the Impact of Risk Mitigation Through Failure Prevention and Correction," Risk Analysis, John Wiley & Sons, vol. 40(10), pages 1928-1943, October.
    2. Goldstein, Michael & Bedford, Tim, 2007. "The Bayes linear approach to inference and decision-making for a reliability programme," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1344-1352.
    3. Marta Cabral & Dália Loureiro & Inês Flores-Colen & Dídia Covas, 2022. "A Distress-Based Condition Assessment Approach of Urban Water Assets Using Novel Deterioration Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1075-1092, February.
    4. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    5. Oosterlinck, Dieter & Benoit, Dries F. & Baecke, Philippe, 2020. "From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1011-1024.
    6. Awat Ghomghaleh & Reza Khaloukakaie & Mohammad Ataei & Abbas Barabadi & Ali Nouri Qarahasanlou & Omeid Rahmani & Amin Beiranvand Pour, 2020. "Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.
    7. Carr, Matthew J. & Wang, Wenbin, 2011. "An approximate algorithm for prognostic modelling using condition monitoring information," European Journal of Operational Research, Elsevier, vol. 211(1), pages 90-96, May.
    8. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
    9. M Revie & T Bedford & L Walls, 2010. "Evaluation of elicitation methods to quantify Bayes linear models," Journal of Risk and Reliability, , vol. 224(4), pages 322-332, December.
    10. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    11. Jingyi Zhao & Chunhai Gao & Tao Tang, 2022. "A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment," Sustainability, MDPI, vol. 14(5), pages 1-22, March.
    12. Si, Xiao-Sheng & Wang, Wenbin & Chen, Mao-Yin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution," European Journal of Operational Research, Elsevier, vol. 226(1), pages 53-66.

    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:eee:reensy:v:130:y:2014:i:c:p:33-41. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.