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A methodology for probabilistic model-based prognosis

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  • Lorton, A.
  • Fouladirad, M.
  • Grall, A.

Abstract

This paper deals with the prognosis of complex systems using stochastic model-based techniques. Prognosis consists in this case in computing the distribution of the Remaining Useful Life (RUL) of the system conditionally to available information. In so doing, three main challenges arise from the industrial context. First, the model should unify the two classical approaches to describing complex systems: the bottom-up and the top-down approaches. The former uses elementary interacting components whilst the latter models the system’s physical behavior by means of a set of differential equations. Second, the prognosis must integrate online information to provide a specific result for each system depending on their life events. Online information can take different forms (e.g. inspections, component faults, non detection or false alarm, noisy signal) which must all be considered. Third, the prognosis must supply ready, meaningful numerical results, the error of which must also be under control. This paper proposes a method addressing those challenges. The method is illustrated with two different examples: a simplified spring-mass system and a pneumatic valve for aeronautical application.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:225:y:2013:i:3:p:443-454
    DOI: 10.1016/j.ejor.2012.10.025
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    References listed on IDEAS

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    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. 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.
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    4. 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.
    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. Dieulle, L. & Berenguer, C. & Grall, A. & Roussignol, M., 2003. "Sequential condition-based maintenance scheduling for a deteriorating system," European Journal of Operational Research, Elsevier, vol. 150(2), pages 451-461, October.
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    Cited by:

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    4. 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.
    5. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
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    7. Zhu, Wenjin & Fouladirad, Mitra & Bérenguer, Christophe, 2016. "A multi-level maintenance policy for a multi-component and multifailure mode system with two independent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 50-63.
    8. 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.
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