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Publication . Conference object . 2018

A Deep Learning Approach to Anomaly Detection in Nuclear Reactors

Caliva, Francesco; Ribeiro, Fabio De Sousa; Mylonakis, Antonios; Demazière, Christophe; Leontidis, Georgios; Kollias, Stefanos;
Open Access
English
Published: 06 Sep 2018
Publisher: Zenodo
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.
Subjects

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

Funded by
EC| CORTEX
Project
CORTEX
Core monitoring techniques and experimental validation and demonstration
  • Funder: European Commission (EC)
  • Project Code: 754316
  • Funding stream: H2020 | RIA
Validated by funder
,
EC| CORTEX
Project
CORTEX
Core monitoring techniques and experimental validation and demonstration
  • Funder: European Commission (EC)
  • Project Code: 754316
  • Funding stream: H2020 | RIA
Validated by funder
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