A Deep Learning Approach to Anomaly Detection in Nuclear Reactors

Francesco Calivá, Fabio Sousa De Ribeiro, Antonios Mylonakis, Christophe Demazirere, Paolo Vinai, Georgios Leontidis, Stefanos Kollias

Research output: Contribution to conferencePaper

14 Citations (Scopus)

Abstract

In this work, a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and k-means clustering of representations. Monitoring nuclear reactors while running at nominal conditions is critical. Based on analysis of the core reactor neutron flux, it is possible to derive useful information for building fault/anomaly detection systems. By leveraging signal and image pre-processing techniques, the high and low energy spectra of the signals were appropriated into a compatible format for CNN training. Firstly, a CNN was employed to unfold the signal into either twelve or forty-eight perturbation location sources, followed by a k-means clustering and k-Nearest Neighbour coarse-to-fine procedure, which significantly increases the unfolding resolution. Secondly, a DAE was utilised to denoise and reconstruct power reactor signals at varying levels of noise and/or corruption. The reconstructed signals were evaluated w.r.t. Their original counter parts, by way of normalised cross correlation and unfolding metrics. The results illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured/noisy signals, across various levels of granularity.

Original languageEnglish
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 15 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • anomaly detection
  • clustering trained representations
  • convolutional neural networks
  • deep learning
  • denoising autoencoders
  • nuclear reactors
  • signal processing
  • unfolding

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