IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v94y2009i10p1618-1628.html
   My bibliography  Save this article

A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data

Author

Listed:
  • Louit, D.M.
  • Pascual, R.
  • Jardine, A.K.S.

Abstract

Many times, reliability studies rely on false premises such as independent and identically distributed time between failures assumption (renewal process). This can lead to erroneous model selection for the time to failure of a particular component or system, which can in turn lead to wrong conclusions and decisions. A strong statistical focus, a lack of a systematic approach and sometimes inadequate theoretical background seem to have made it difficult for maintenance analysts to adopt the necessary stage of data testing before the selection of a suitable model. In this paper, a framework for model selection to represent the failure process for a component or system is presented, based on a review of available trend tests. The paper focuses only on single-time-variable models and is primarily directed to analysts responsible for reliability analyses in an industrial maintenance environment. The model selection framework is directed towards the discrimination between the use of statistical distributions to represent the time to failure (“renewal approach†); and the use of stochastic point processes (“repairable systems approach†), when there may be the presence of system ageing or reliability growth. An illustrative example based on failure data from a fleet of backhoes is included.

Suggested Citation

  • Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:10:p:1618-1628
    DOI: 10.1016/j.ress.2009.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832009000891
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2009.04.001?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. Percy, David F. & Kobbacy, Khairy A. H. & Fawzi, Bahir B., 1997. "Setting preventive maintenance schedules when data are sparse," International Journal of Production Economics, Elsevier, vol. 51(3), pages 223-234, September.
    2. Scarf, Philip A., 1997. "On the application of mathematical models in maintenance," European Journal of Operational Research, Elsevier, vol. 99(3), pages 493-506, June.
    3. Viertävä, Janne & Vaurio, Jussi K., 2009. "Testing statistical significance of trends in learning, ageing and safety indicators," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1128-1132.
    4. J. I. Ansell & M. J. Phillips, 1989. "Practical Problems in the Statistical Analysis of Reliability Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(2), pages 205-231, June.
    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. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    2. Percy, David F. & Kobbacy, Khairy A. H., 2000. "Determining economical maintenance intervals," International Journal of Production Economics, Elsevier, vol. 67(1), pages 87-94, August.
    3. Percy, David F., 2002. "Bayesian enhanced strategic decision making for reliability," European Journal of Operational Research, Elsevier, vol. 139(1), pages 133-145, May.
    4. Peters, Lennart & Madlener, Reinhard, 2017. "Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants," Applied Energy, Elsevier, vol. 199(C), pages 264-280.
    5. Kuschel, Torben & Bock, Stefan, 2016. "The weighted uncapacitated planned maintenance problem: Complexity and polyhedral properties," European Journal of Operational Research, Elsevier, vol. 250(3), pages 773-781.
    6. Wu, Shaomin & Scarf, Philip, 2015. "Decline and repair, and covariate effects," European Journal of Operational Research, Elsevier, vol. 244(1), pages 219-226.
    7. Guo R. & Ascher H. & Love E., 2001. "Towards Practical and Synthetical Modelling of Repairable Systems," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 147-182, January.
    8. Zhicheng Zhu & Yisha Xiang & Bo Zeng, 2021. "Multicomponent Maintenance Optimization: A Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 898-914, July.
    9. Dimitrakos, T.D. & Kyriakidis, E.G., 2008. "A semi-Markov decision algorithm for the maintenance of a production system with buffer capacity and continuous repair times," International Journal of Production Economics, Elsevier, vol. 111(2), pages 752-762, February.
    10. Wang, Ling & Xu, Hong & Yuan, Hua & Zhao, Wenjie & Chen, Xiai, 2015. "Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model," European Journal of Operational Research, Elsevier, vol. 242(3), pages 975-986.
    11. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.
    12. Kurt, Murat & Kharoufeh, Jeffrey P., 2010. "Optimally maintaining a Markovian deteriorating system with limited imperfect repairs," European Journal of Operational Research, Elsevier, vol. 205(2), pages 368-380, September.
    13. Alberti, Alexandre R. & Cavalcante, Cristiano A.V. & Scarf, Philip & Silva, André L.O., 2018. "Modelling inspection and replacement quality for a protection system," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 145-153.
    14. Santos, Augusto César de Jesus & Cavalcante, Cristiano Alexandre Virgínio, 2022. "A study on the economic and environmental viability of second-hand items in maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    15. Markus Bohlin & Mathias Wärja, 2015. "Maintenance optimization with duration-dependent costs," Annals of Operations Research, Springer, vol. 224(1), pages 1-23, January.
    16. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    17. Cavalcante, C.A.V. & Lopes, R.S. & Scarf, P.A., 2018. "A general inspection and opportunistic replacement policy for one-component systems of variable quality," European Journal of Operational Research, Elsevier, vol. 266(3), pages 911-919.
    18. Syamsundar, A. & Naikan, V.N.A. & Wu, Shaomin, 2021. "Extended Arithmetic Reduction of Age Models for the Failure Process of a Repairable System," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Kevin Doyle, E., 2004. "On the application of stochastic models in nuclear power plant maintenance," European Journal of Operational Research, Elsevier, vol. 154(3), pages 673-690, May.
    20. Cocozza-Thivent, Christiane, 2007. "MUT of a one out of two system with preventive maintenance," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1203-1229, November.

    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:94:y:2009:i:10:p:1618-1628. 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.