IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v227y2013i4p434-449.html
   My bibliography  Save this article

Computation of remaining useful life on a physic-based model and impact of a prognosis on the maintenance process

Author

Listed:
  • Ariane Lorton
  • Mitra Fouladirad
  • Antoine Grall

Abstract

The aim of this article is double: propose a methodology for a probabilistic prognosis, and examine how the prognosis result impacts the maintenance process. First, the prognosis problem is mathematically defined: it consists in computing the distribution of the remaining useful life of the system conditionally to on-line available information. Considering on-line information allows to provide a specific prognosis for each system according to its life. Second, a global methodology is proposed when the state of the system and its degradations are modeled using a Markov process. This method is basically a two-step technique. On one hand, it requires the computation of the conditional law of the system regarding the available observations. On the other hand, it involves the computation of the reliability of the system. Some reliability computation techniques are proposed when the Markov process is a piecewise deterministic Markov process. The method is illustrated on an aeronautic example: a pneumatic valve within the bleed air system, used to provide regulated air (pressure, temperature) in the cabin. Eventually, the prognosis result is used to help maintenance optimization on an illustrative example. It highlights that the prognosis mainly improves the maintenance decision if the on-line available information is accurate enough.

Suggested Citation

  • Ariane Lorton & Mitra Fouladirad & Antoine Grall, 2013. "Computation of remaining useful life on a physic-based model and impact of a prognosis on the maintenance process," Journal of Risk and Reliability, , vol. 227(4), pages 434-449, August.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:4:p:434-449
    DOI: 10.1177/1748006X13481926
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X13481926
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X13481926?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. Khac Tuan Huynh & Inma T. Castro & Anne Barros & Christophe Bérenguer, 2012. "Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks," Post-Print hal-00790729, HAL.
    2. Christiane Cocozza-Thivent & Vladimir Kalashnikov, 1996. "The failure rate in reliability: approximations and bounds," International Journal of Stochastic Analysis, Hindawi, vol. 9, pages 1-34, January.
    3. Huynh, K.T. & Castro, I.T. & Barros, A. & Bérenguer, C., 2012. "Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks," European Journal of Operational Research, Elsevier, vol. 218(1), pages 140-151.
    4. Ponchet, Amélie & Fouladirad, Mitra & Grall, Antoine, 2010. "Assessment of a maintenance model for a multi-deteriorating mode system," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1244-1254.
    5. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    6. Lorton, A. & Fouladirad, M. & Grall, A., 2013. "A methodology for probabilistic model-based prognosis," European Journal of Operational Research, Elsevier, vol. 225(3), pages 443-454.
    7. Ariane Lorton & Mitra Fouladirad & Antoine Grall, 2013. "A methodology for probabilistic model-based prognosis," Post-Print hal-02284358, HAL.
    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. Nguyen, Khanh T.P. & Fouladirad, Mitra & Grall, Antoine, 2018. "Model selection for degradation modeling and prognosis with health monitoring data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 105-116.
    2. Langeron, Y. & Grall, A. & Barros, A., 2015. "A modeling framework for deteriorating control system and predictive maintenance of actuators," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 22-36.
    3. Lin, Yan-Hui & Li, Yan-Fu & Zio, Enrico, 2018. "A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 1-11.

    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. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    2. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    3. Wang, Xiaolin & Liu, Bin & Zhao, Xiujie, 2021. "A performance-based warranty for products subject to competing hard and soft failures," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Giorgio, Massimiliano & Pulcini, Gianpaolo, 2018. "A new state-dependent degradation process and related model misidentification problems," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1027-1038.
    5. 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.
    6. N. C. Caballé & I. T. Castro, 2019. "Assessment of the maintenance cost and analysis of availability measures in a finite life cycle for a system subject to competing failures," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 255-290, March.
    7. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    8. Mahmood Shafiee & Maxim Finkelstein, 2015. "A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines," Journal of Risk and Reliability, , vol. 229(5), pages 373-384, October.
    9. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2018. "Optimal imperfect maintenance cost analysis of a two-component system with failure interactions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 24-34.
    10. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
    11. Shafiee, Mahmood & Finkelstein, Maxim, 2015. "An optimal age-based group maintenance policy for multi-unit degrading systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 230-238.
    12. Roy Assaf & Phuc Do & Samia Nefti-Meziani & Philip Scarf, 2018. "Wear rate–state interactions within a multi-component system: a study of a gearbox-accelerated life testing platform," Journal of Risk and Reliability, , vol. 232(4), pages 425-434, August.
    13. Lorton, A. & Fouladirad, M. & Grall, A., 2013. "A methodology for probabilistic model-based prognosis," European Journal of Operational Research, Elsevier, vol. 225(3), pages 443-454.
    14. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    15. Tsai, Hsin-Nan & Sheu, Shey-Huei & Zhang, Zhe George, 2017. "A trivariate optimal replacement policy for a deteriorating system based on cumulative damage and inspections," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 74-88.
    16. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    17. Hyunju Lee & Ji Hwan Cha, 2021. "A general multivariate new better than used (MNBU) distribution and its properties," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 27-46, January.
    18. 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.
    19. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    20. Dahal, Keshav & Al-Arfaj, Khalid & Paudyal, Krishna, 2015. "Modelling generator maintenance scheduling costs in deregulated power markets," European Journal of Operational Research, Elsevier, vol. 240(2), pages 551-561.

    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:227:y:2013:i:4:p:434-449. 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.