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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Annals of Nuclear En...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Annals of Nuclear Energy
Article . 2020 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models

Authors: Poong Hyun Seong; Hyeonmin Kim; Seung Geun Kim; Young Ho Chae; Jung Taek Kim;

A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models

Abstract

Abstract The flow accelerated corrosion (FAC) phenomenon is critical phenomenon that undermine the safety of nuclear power plant. The problems of FAC are not only induces pipe’s thinning but occurs widely in nuclear power plants. Therefore, precise diagnosis of FAC phenomenon is important but the FAC phenomenon has a complicated mechanism. As a result, accurately diagnosing the FAC phenomenon is difficult (Kain, 2014). To overcome these difficulties, we proposed a methodology utilizing vibration data and deep learning to diagnose FAC induced thinning. To minimize the effect of outliers, Cook’s distance and the Hilbert transform were used. When there was a significant difference in the pipes’ thickness, the support vector machine (SVM), convolutional neural network (CNN), and long-short term memory (LSTM) network all showed good results. However, when the difference in pipes’ thickness was subtle and thinning was non-uniform, only LSTM successfully diagnosed the pipe’s condition.

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    34
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
34
Top 10%
Top 10%
Top 10%