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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    <dct:title>A Deep Learning Approach to Anomaly Detection in Nuclear Reactors</dct:title>
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    <dcat:keyword>deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection</dcat:keyword>
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    <dct:description>&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;</dct:description>
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