Conference paper Open Access

A Deep Learning Approach to Anomaly Detection in Nuclear Reactors

Francesco Caliva; Fabio De Sousa Ribeiro; Antonios Mylonakis; Christophe Demazière; Georgios Leontidis; Stefanos Kollias


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  <identifier identifierType="DOI">10.5281/zenodo.1410084</identifier>
  <creators>
    <creator>
      <creatorName>Francesco Caliva</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Fabio De Sousa Ribeiro</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Antonios Mylonakis</creatorName>
      <affiliation>Chalmers University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Christophe Demazière</creatorName>
      <affiliation>Chalmers University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Georgios Leontidis</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Stefanos Kollias</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Deep Learning Approach to Anomaly Detection in Nuclear Reactors</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-06</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1410084</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1410083</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this work, a novel deep learning approach to&lt;br&gt;
unfold nuclear power reactor signals is proposed. It includes a&lt;br&gt;
combination of convolutional neural networks (CNN), denoising&lt;br&gt;
autoencoders (DAE) and k-means clustering of representations.&lt;br&gt;
Monitoring nuclear reactors while running at nominal conditions&lt;br&gt;
is critical. Based on analysis of the core reactor neutron flux, it is&lt;br&gt;
possible to derive useful information for building fault/anomaly&lt;br&gt;
detection systems. By leveraging signal and image pre-processing&lt;br&gt;
techniques, the high and low energy spectra of the signals were&lt;br&gt;
appropriated into a compatible format for CNN training. Firstly,&lt;br&gt;
a CNN was employed to unfold the signal into either twelve or&lt;br&gt;
forty-eight perturbation location sources, followed by a k-means&lt;br&gt;
clustering and k-Nearest Neighbour coarse-to-fine procedure,&lt;br&gt;
which significantly increases the unfolding resolution. Secondly, a&lt;br&gt;
DAE was utilised to denoise and reconstruct power reactor signals&lt;br&gt;
at varying levels of noise and/or corruption. The reconstructed&lt;br&gt;
signals were evaluated w.r.t. their original counter parts, by way&lt;br&gt;
of normalised cross correlation and unfolding metrics. The results&lt;br&gt;
illustrate that the origin of perturbations can be localised with&lt;br&gt;
high accuracy, despite limited training data and obscured/noisy&lt;br&gt;
signals, across various levels of granularity.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/754316/">754316</awardNumber>
      <awardTitle>Core monitoring techniques and experimental validation and demonstration</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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