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Lifecycle Prognostics: Transitioning between information types

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  • Michael Sharp
  • Jamie Coble
  • Alan Nam
  • J Wes Hines
  • Belle Upadhyaya

Abstract

As nuclear power plants seek to extend their licenses and maintain a high level of performance and safety, online monitoring and assessment of system degradation are becoming a crucial consideration. A goal of the DOE Light Water Reactor Sustainability program is the accurate estimation of the remaining useful life of nuclear power plant systems, structures, and components. Effective prognostic systems should seamlessly predict the remaining useful life from beginning of component life to end of component life, so-called Lifecycle Prognostics. When a component is first put into operation, the only information available may be past failure times of similar components or the expected distribution of failure times derived from reliability analyses of these data (Type I Prognostics). These data provide an estimated life for the average component operating under average usage conditions. As the component operates, it begins to consume its available life at a rate largely influenced by the system and environmental stresses. Information from these recorded stresses can be used to update the expected failure time distribution (Type II Prognostics). The incorporation of stressor information allows for the estimation of the remaining useful life for an average component operating under specific usage conditions. After continued operations, measurable levels of degradation may evolve, which allows for further improvement of the failure time distribution estimate by incorporating these health indicators (Type III Prognostics). This article presents a framework for using Bayesian methods to transition between prognostic model types and to update failure time distribution estimates as new information becomes available.

Suggested Citation

  • Michael Sharp & Jamie Coble & Alan Nam & J Wes Hines & Belle Upadhyaya, 2015. "Lifecycle Prognostics: Transitioning between information types," Journal of Risk and Reliability, , vol. 229(4), pages 279-290, August.
    • Handle: RePEc:sae:risrel:v:229:y:2015:i:4:p:279-290
    DOI: 10.1177/1748006X14557110
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    References listed on IDEAS

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    1. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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