Conference paper Open Access
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
Fabio De Sousa Ribeiro; Francesco Caliva; Dionysios Chionis; Abdelhamid Dokhane; Antonios Mylonakis; Christophe Demaziere; Georgios Leontidis; Stefanos Kollias
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.
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- Publication date:
- November 21, 2018
- DOI:
-
- Keyword(s):
- deep learning, 3D convolutional neural networks, recurrent neural networks, long short-term memory, multi label classification, regression, signal processing, nuclear reactors, unfolding, anomaly detection
- Grants:
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European Commission:
- CORTEX - Core monitoring techniques and experimental validation and demonstration (754316)
- Meeting:
- IEEE Symposium Series on Computational Intelligence (SSCI 2018), Bengaluru, India, November 18 – 21, 2018
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International