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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Ariane Lorton & Mitra Fouladirad & Antoine Grall, 2013. "A methodology for probabilistic model-based prognosis," Post-Print hal-02284358, HAL.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    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. 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.
    3. 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.

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