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2016 | OriginalPaper | Buchkapitel

Improving Online Risk Assessment with Equipment Prognostics and Health Monitoring

verfasst von : Jamie Coble, Xiaotong Liu, Chris Briere, Pradeep Ramuhalli

Erschienen in: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015)

Verlag: Springer International Publishing

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Abstract

The current approach to evaluating the risk of nuclear power plant (NPP) operation relies on static probabilities of component failure, which are based on industry experience with the existing fleet of nominally similar light water reactors (LWRs). As the nuclear industry looks to advanced reactor designs that feature non-light water coolants (e.g., liquid metal, high temperature gas, molten salt), this operating history is not available. Many advanced reactor designs use advanced components, such as electromagnetic pumps, that have not been used in the US commercial nuclear fleet. Given the lack of rich operating experience, we cannot accurately estimate the evolving probability of failure for basic components to populate the fault trees and event trees that typically comprise probabilistic risk assessment (PRA) models. Online equipment prognostics and health management (PHM) technologies can bridge this gap to estimate the failure probabilities for components under operation. The enhanced risk monitor (ERM) incorporates equipment condition assessment into the existing PRA and risk monitor framework to provide accurate and timely estimates of operational risk.

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Metadaten
Titel
Improving Online Risk Assessment with Equipment Prognostics and Health Monitoring
verfasst von
Jamie Coble
Xiaotong Liu
Chris Briere
Pradeep Ramuhalli
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-27064-7_14