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Title: ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA

Abstract

In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant equipment, which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component health are not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving over time. There is a need to develop enabling approaches to solidify risk monitors to provide time and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques. This paper presents estimation of system risk evolution over time by integrating plant risk monitoring data with dynamic PRA methods incorporating aging and degradation. Several online, non-destructive approaches have been developed for diagnosing plant component conditions in nuclear industry, i.e., condition indication index, using vibration analysis, current signatures, and operational history [1].more » In this work the component performance measures at U.S. commercial nuclear power plants (NPP) [2] are incorporated within the various dynamic PRA methodologies [3] to provide better estimates of probability of failures. Aging and degradation is modeled within the Level-1 PRA framework and is applied to several failure modes of pumps and can be extended to a range of components, viz. valves, generators, batteries, and pipes.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1375214
Report Number(s):
INL/CON-17-41335
DOE Contract Number:  
DE-AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, (NPIC & HMIT), San Francisco, CA, USA, June 11–15, 2017
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; component aging and degradation; dynamic probabilistic risk assessment; Markov-chain

Citation Formats

Yadav, Vaibhav, Agarwal, Vivek, Gribok, Andrei V., and Smith, Curtis L. ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA. United States: N. p., 2017. Web.
Yadav, Vaibhav, Agarwal, Vivek, Gribok, Andrei V., & Smith, Curtis L. ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA. United States.
Yadav, Vaibhav, Agarwal, Vivek, Gribok, Andrei V., and Smith, Curtis L. Thu . "ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA". United States. doi:. https://www.osti.gov/servlets/purl/1375214.
@article{osti_1375214,
title = {ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA},
author = {Yadav, Vaibhav and Agarwal, Vivek and Gribok, Andrei V. and Smith, Curtis L.},
abstractNote = {In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant equipment, which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component health are not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving over time. There is a need to develop enabling approaches to solidify risk monitors to provide time and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques. This paper presents estimation of system risk evolution over time by integrating plant risk monitoring data with dynamic PRA methods incorporating aging and degradation. Several online, non-destructive approaches have been developed for diagnosing plant component conditions in nuclear industry, i.e., condition indication index, using vibration analysis, current signatures, and operational history [1]. In this work the component performance measures at U.S. commercial nuclear power plants (NPP) [2] are incorporated within the various dynamic PRA methodologies [3] to provide better estimates of probability of failures. Aging and degradation is modeled within the Level-1 PRA framework and is applied to several failure modes of pumps and can be extended to a range of components, viz. valves, generators, batteries, and pipes.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 01 00:00:00 EDT 2017},
month = {Thu Jun 01 00:00:00 EDT 2017}
}

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