publication . Conference object . Article . 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
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 ...
Subjects
free text keywords: deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection, 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
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Conference object . 2018
Provider: Datacite
Open Access
Zenodo
Conference object . 2018
Provider: Datacite
Open Access
ZENODO
Conference object . 2018
Provider: ZENODO
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