Towards a deep unified framework for nuclear reactor perturbation analysis
Paper i proceeding, 2019

In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring reactor cores and guarantee safety while running at nominal conditions. A 3D Convolutional Neural Network (3DCNN) was employed to analyse perturbations happening in the frequency domain, such as an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) networks were used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuations of thermal-hydraulic parameters at the inlet of the reactor coolant loops. 512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type. If the perturbation is in the frequency domain, a separate fully-connected layer utilises said representations to regress the coordinates of its source. The results showed that the perturbation type can be recognised with high accuracy in all cases, and frequency domain scenario sources can be localised with high precision.

deep learning

signal processing

anomaly detection


long short-term memory

3D convolutional neural networks

multi label classification

recurrent neural networks


nuclear reactors


Fabio De Sousa Ribeiro

University of Lincoln

Francesco Calivá

University of Lincoln

Dionysios Chionis

Paul Scherrer Institut

Adbelhamid Dokhane

Paul Scherrer Institut

Antonios Mylonakis

Chalmers, Fysik, Subatomär fysik och plasmafysik

Christophe Demaziere

Chalmers, Fysik, Subatomär fysik och plasmafysik

Georgios Leontidis

University of Lincoln

Stefanos Kollias

University of Lincoln

2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

978-1-5386-9276-9 (ISBN)

8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
Bengaluru, India,

Core monitoring techniques and experimental validation and demonstration (CORTEX)

Europeiska kommissionen (EU) (EC/H2020/754316), 2017-09-01 -- 2021-08-31.




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