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Title: A Bayesian Prognostic Algorithm for Assessing Remaining Useful Life of Nuclear Power Components

Abstract

A central issue in life extension for the current fleet of light water nuclear power reactors is the early detection and monitoring of significant materials degradation. To meet this need nondestructive measurement methods that are suitable for on-line, continuous, in-plant monitoring over extended time periods (months to years) are needed. A related issue is then, based on a condition assessment or degradation trend, to have the ability to estimate the remaining useful life based of components, structures and systems based on the available materials degradation information. Such measurement and modeling methods form the basis for a new range of advanced diagnostic and prognostic approaches. Prognostic methods that predict remaining life based on large crack growth, and phenomena that can be described by linear elastic fracture mechanics, have been reported by several researchers. The challenge of predicting remaining life for earlier phases of degradation is largely unsolved. Monitoring for early detection of materials degradation requires novel and enhanced sensors and data integration techniques. A recent review has considered the stages of degradation and sensing methods that can potentially be employed to detect and monitor early degradation for nuclear power plant applications. An experimental assessment of selected diagnostic techniques was also reportedmore » recently. However, the estimation of remaining useful life (RUL) determined from nondestructive diagnostic measurements for early degradation is still an unsolved problem. This present paper will discuss the application of Bayesian prognostic algorithms applied to the early degradation- life problem.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1118126
Report Number(s):
PNNL-SA-74475
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 7th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2010), November 7-11, 2010, Las Vegas, Nevada, 2:875-886
Country of Publication:
United States
Language:
English
Subject:
Bayesian prognostics; early degradation monitoring; mechanical fatigue; LWR life extension

Citation Formats

Ramuhalli, Pradeep, Bond, Leonard J., Griffin, Jeffrey W., Dixit, Mukul, and Henager, Charles H. A Bayesian Prognostic Algorithm for Assessing Remaining Useful Life of Nuclear Power Components. United States: N. p., 2010. Web.
Ramuhalli, Pradeep, Bond, Leonard J., Griffin, Jeffrey W., Dixit, Mukul, & Henager, Charles H. A Bayesian Prognostic Algorithm for Assessing Remaining Useful Life of Nuclear Power Components. United States.
Ramuhalli, Pradeep, Bond, Leonard J., Griffin, Jeffrey W., Dixit, Mukul, and Henager, Charles H. Wed . "A Bayesian Prognostic Algorithm for Assessing Remaining Useful Life of Nuclear Power Components". United States.
@article{osti_1118126,
title = {A Bayesian Prognostic Algorithm for Assessing Remaining Useful Life of Nuclear Power Components},
author = {Ramuhalli, Pradeep and Bond, Leonard J. and Griffin, Jeffrey W. and Dixit, Mukul and Henager, Charles H.},
abstractNote = {A central issue in life extension for the current fleet of light water nuclear power reactors is the early detection and monitoring of significant materials degradation. To meet this need nondestructive measurement methods that are suitable for on-line, continuous, in-plant monitoring over extended time periods (months to years) are needed. A related issue is then, based on a condition assessment or degradation trend, to have the ability to estimate the remaining useful life based of components, structures and systems based on the available materials degradation information. Such measurement and modeling methods form the basis for a new range of advanced diagnostic and prognostic approaches. Prognostic methods that predict remaining life based on large crack growth, and phenomena that can be described by linear elastic fracture mechanics, have been reported by several researchers. The challenge of predicting remaining life for earlier phases of degradation is largely unsolved. Monitoring for early detection of materials degradation requires novel and enhanced sensors and data integration techniques. A recent review has considered the stages of degradation and sensing methods that can potentially be employed to detect and monitor early degradation for nuclear power plant applications. An experimental assessment of selected diagnostic techniques was also reported recently. However, the estimation of remaining useful life (RUL) determined from nondestructive diagnostic measurements for early degradation is still an unsolved problem. This present paper will discuss the application of Bayesian prognostic algorithms applied to the early degradation- life problem.},
doi = {},
url = {https://www.osti.gov/biblio/1118126}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {12}
}

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