Local-Level Prognostics Health Management Systems Framework for Passive AdvSMR Components. Interim Report

Ramuhalli, Pradeep; Roy, Surajit; Hirt, Evelyn H.; Pardini, Allan F.; Jones, Anthony M.; Deibler, John E.; Pitman, Stan G.; Tucker, Joseph C.; Prowant, Matthew S.; Suter, Jonathan D.

doi:10.2172/1168935

Cited by 3 publications

(11 citation statements)

References 51 publications

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“…A number of technical requirements for PHM systems in ARs were identified based on these gaps, and technologies were developed to address several of these requirements. These technologies are documented in other reports in this series (Ramuhalli et al 2014b;Meyer et al 2013a;Meyer et al 2013d).…”

Section: Prognostic Health Management For Advanced Reactorssupporting

confidence: 77%

“…In this research, the focus is on further developing the framework for the lower-level PHM systems. This framework, and a potential approach to performing prognostics, is described in Ramuhalli et al (2014b). Figure 2.1 illustrates a system-ofsystems approach, with individual PHM systems monitoring the different SSCs, with additional systems in the hierarchy integrating the output from the individual PHM systems and providing the necessary interfaces to plant supervisory control systems, operational risk monitors, and O&M decision making.…”

Section: Prototypic Prognostic Methodologymentioning

confidence: 99%

“…Research conducted to date on PHM systems in ARs and AdvSMRs was previously documented in a series of technical reports (Ramuhalli et al 2014b;Meyer et al 2013a;Meyer et al 2013d). Meyer et al (2013a) discussed a number of technical gaps that limit the applicability of current PHM systems in ARs.…”

Section: Prognostic Health Management For Advanced Reactorsmentioning

confidence: 99%

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Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Ramuhalli¹,

Roy²,

Hirt³

et al. 2015

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Prognostic health management technologies are expected to play a vital role in the deployment and safe, cost-effective operation of advanced reactors by providing the technical means for lifetime management of significant passive components and reactor internals. This report describes a Bayesian methodology for the prediction of remaining life of materials and passive AR components. This approach, previously applied to predict time-to-failure of materials subjected to localized aging and degradation, is adapted for component-level prognostics. The Bayesian framework for component-level prognostics incorporates the ability to fuse information from multiple sources, including information on localized degradation and component-level condition indicators. The ability to switch between multiple models of degradation accumulation rate and/or multiple models of measurement physics becomes important in this context, and a reversible jump Markov chain Monte Carlo methodology has been developed for this purpose. Evaluations of the Bayesian framework and the model switching and selection methodology were performed using synthetic data as well as experimental measurements on a high-temperature creep testbed. Results to date indicate that the feasibility of the proposed Bayesian framework for prognostics, though an improvement over previous methods' accuracy, will require the ability to quantify sources of uncertainties within the various models used in the prognostic framework. Ongoing efforts are focused on sensing and measurement (particularly in-situ measurements) that would be applied as inputs to the prognostics framework, with the objective of identifying measurement methods that can provide early indicators of material degradation and quantifying the reliability and sensitivity of these measurement methods.

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Section: Prognostic Health Management For Advanced Reactorssupporting

confidence: 77%

Section: Prototypic Prognostic Methodologymentioning

confidence: 99%

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Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Ramuhalli¹,

Roy²,

Hirt³

et al. 2015

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“…A number of technical requirements for PHM systems in AdvRx were identified based on these gaps, and technologies were developed to address several of these requirements. These technologies are documented in several previous reports in this series (Meyer et al 2013a;Meyer et al 2013c;Ramuhalli et al 2014b;Ramuhalli et al 2015).…”

Section: Research Objectivessupporting

confidence: 71%

Experimental Design for Evaluating Selected Nondestructive Measurement Technologies - Advanced Reactor Technology Milestone: M3AT-16PN2301043

Ramuhalli

Hirt

Pitman

et al. 2016

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The harsh environments in advanced reactors (AdvRx) increase the possibility of degradation of safetycritical passive components and therefore pose a particular challenge for deployment and extended operation of these concepts. Nondestructive evaluation technologies are an essential element for obtaining information on passive component condition in AdvRx, with the development of sensor technologies for nondestructively inspecting AdvRx passive components identified as a key need. Given the challenges posed by AdvRx environments and the potential needs for reducing the burden posed by periodic inservice inspection of hard-to-access and hard-to-replace components, a viable solution may be provided by online condition monitoring of components. This report identifies the key challenges that will need to be overcome for sensor development in this context and documents an experimental plan for sensor development, test, and evaluation. The focus of initial research and development is on sodium fast reactors with the eventual goal of the research being developing the necessary sensor technology, quantifying sensor survivability and long-term measurement reliability for nondestructively inspecting critical components. Materials for sensor development that are likely to withstand the harsh environments are described, along with a status on the fabrication of reference specimens, and the planned approach for design and evaluation of the sensor and measurement technology. v USV under-sodium viewing VHTR very-high-temperature gas reactor xi

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“…2(b). The test bed allows specimens to be removed after a defined amount of time and measured using advanced NDE techniques [20]. Specimens can be re-inserted for inducing additional creep damage if needed.…”

Section: Experimental Set-upmentioning

confidence: 99%

Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation

Roy

Dib

Ramuhalli

et al. 2015

2015 IEEE Conference on Prognostics and Health Management (PHM)

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This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

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Local-Level Prognostics Health Management Systems Framework for Passive AdvSMR Components. Interim Report

Cited by 3 publications

References 51 publications

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Experimental Design for Evaluating Selected Nondestructive Measurement Technologies - Advanced Reactor Technology Milestone: M3AT-16PN2301043

Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation

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Local-Level Prognostics Health Management Systems Framework for Passive AdvSMR Components. Interim Report

Cited by 3 publications

References 51 publications

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Experimental Design for Evaluating Selected Nondestructive Measurement Technologies - Advanced Reactor Technology Milestone: M3AT-16PN2301043

Progress towards prognostic health management of passive components in advanced reactors &#x2014; Model selection and evaluation

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Progress towards prognostic health management of passive components in advanced reactors — Model selection and evaluation