A deep learning approach to anomaly detection in nuclear reactors

Caliva, Francesco and De Sousa Ribeiro, Fabio and Mylonakis, Antonios and Demaziere, Christophe and Vinai, Paolo and Leontidis, Georgios and Kollias, Stefanos (2018) A deep learning approach to anomaly detection in nuclear reactors. 2018 International Joint Conference on Neural Networks (IJCNN) . ISSN 2161-4393

Full content URL: https://doi.org/10.1109/IJCNN.2018.8489130

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A deep learning approach to anomaly detection in nuclear reactors
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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.

Additional Information:©2018 IEEE
Keywords:Deep Learning, Signal Processing, Machine Learning, Anomaly Detection, Nuclear Reactors
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:31359
Deposited On:10 Apr 2018 08:01

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