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


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>In this work, a novel deep learning approach to<br>\nunfold nuclear power reactor signals is proposed. It includes a<br>\ncombination of convolutional neural networks (CNN), denoising<br>\nautoencoders (DAE) and k-means clustering of representations.<br>\nMonitoring nuclear reactors while running at nominal conditions<br>\nis critical. Based on analysis of the core reactor neutron flux, it is<br>\npossible to derive useful information for building fault/anomaly<br>\ndetection systems. By leveraging signal and image pre-processing<br>\ntechniques, the high and low energy spectra of the signals were<br>\nappropriated into a compatible format for CNN training. Firstly,<br>\na CNN was employed to unfold the signal into either twelve or<br>\nforty-eight perturbation location sources, followed by a k-means<br>\nclustering and k-Nearest Neighbour coarse-to-fine procedure,<br>\nwhich significantly increases the unfolding resolution. Secondly, a<br>\nDAE was utilised to denoise and reconstruct power reactor signals<br>\nat varying levels of noise and/or corruption. The reconstructed<br>\nsignals were evaluated w.r.t. their original counter parts, by way<br>\nof normalised cross correlation and unfolding metrics. The results<br>\nillustrate that the origin of perturbations can be localised with<br>\nhigh accuracy, despite limited training data and obscured/noisy<br>\nsignals, across various levels of granularity.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "University of Lincoln", 
      "@type": "Person", 
      "name": "Francesco Caliva"
    }, 
    {
      "affiliation": "University of Lincoln", 
      "@type": "Person", 
      "name": "Fabio De Sousa Ribeiro"
    }, 
    {
      "affiliation": "Chalmers University of Technology", 
      "@type": "Person", 
      "name": "Antonios Mylonakis"
    }, 
    {
      "affiliation": "Chalmers University of Technology", 
      "@type": "Person", 
      "name": "Christophe Demazi\u00e8re"
    }, 
    {
      "affiliation": "University of Lincoln", 
      "@type": "Person", 
      "name": "Georgios Leontidis"
    }, 
    {
      "affiliation": "University of Lincoln", 
      "@type": "Person", 
      "name": "Stefanos Kollias"
    }
  ], 
  "headline": "A Deep Learning Approach to Anomaly Detection in Nuclear Reactors", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-09-06", 
  "url": "https://zenodo.org/record/1410084", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1410084", 
  "@id": "https://doi.org/10.5281/zenodo.1410084", 
  "workFeatured": {
    "alternateName": "IJCNN", 
    "location": "Rio de Janeiro", 
    "@type": "Event", 
    "name": "Joint Conference on Neural Networks"
  }, 
  "name": "A Deep Learning Approach to Anomaly Detection in Nuclear Reactors"
}
52
137
views
downloads
All versions This version
Views 5252
Downloads 137137
Data volume 388.9 MB388.9 MB
Unique views 5252
Unique downloads 133133

Share

Cite as