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3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Authors: Aiden Durrant; Georgios Leontidis; Stefanos Kollias;

3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Abstract

With Europe's ageing fleet of nuclear reactors operating closer to their safety limits, the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. Therefore, we propose an extended Deep Learning framework as part of the CORTEX Horizon 2020 EU project for the unfolding of reactor transfer functions from induced neutron noise sources. The unfolding allows for the identification and localisation of reactor core perturbation sources from neutron detector readings in Pressurised Water Reactors. A 3D Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) have been presented, each to study the signals presented in frequency and time domain respectively. The proposed approach achieves state-of-the-art results with the classification of perturbation type in the frequency domain reaching 99.89% accuracy and localisation of the classified perturbation source being regressed to 0.2902 Mean Absolute Error (MAE).

Country
United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Convolutional neural network Time domain Signal processing business.industry Deep learning Recurrent neural network Nuclear reactor core Frequency domain Anomaly detection Artificial intelligence business Algorithm

Library of Congress Subject Headings: lcsh:TK9001-9401 lcsh:Nuclear engineering. Atomic power

Keywords

Reactor safety, modeling, CORTEX, TK9001-9401, G400 Computer Science, Nuclear engineering. Atomic power, H821 Nuclear Engineering, G760 Machine Learning

20 references, page 1 of 2

1. C. Demazière et al., Overview of the CORTEX project, in Proc. Int. Conf. Physics of Reactors Reactor Physics paving the way towards more efficient systems (PHYSOR2018), Cancun, Mexico, April 22-26, 2018 (2018)

2. D. Rolnick et al., Tackling Climate Change with Machine Learning, arXiv:1906.05433 (2019)

3. F.C. Chen, M.R. Jahanshahi, NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion, IEEE Trans. Ind. Electron 65, 4392 (2017)

4. W. Li et al., Design of comprehensive diagnosis system in nuclear power plant, Ann. Nucl. Energy 109, 92 (2017)

5. M.C. dos Santos et al., Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant, Ann. Nucl. Energy 133, 400 (2019)

6. R.M. Ayo-Imoru, A.C. Cilliers, Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant, Ann. Nucl. Energy 118, 61 (2018)

7. S. Zaferanlouei et al., Prediction of critical heat flux using anfis, Ann. Nucl. Energy 37, 813 (2010)

8. S.A. Hosseini, I.E.P. Afrakoti, Neutron noise source reconstruction using the adaptive neuro-fuzzy inference system (anfis) in the vver-1000 reactor core, Ann. Nucl. Energy 105, 36 (2017)

9. S.A. Hosseini, I.E.P. Afrakoti, Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive NeuroFuzzy Inference System (ANFIS), J. Radiat. Res. 59, 436 (2018)

10. F. Calivà et al., A deep learning approach to anomaly detection in nuclear reactors, in Proc. 2018 Int. Joint Conf. Neural Networks (IJCNN2018), Rio de Janeiro, Brazil, July 8-13, 2018 (2018) [OpenAIRE]

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    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
8
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Average
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61
106
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