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Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis. (arXiv:1807.10096v1 [cs.LG])

This paper proposes the first step towards a novel unified framework for the
analysis of perturbations occurring in nuclear reactors in both Time and
Frequency domain. The identification of type and source of such perturbations
is fundamental for monitoring core reactors and guarantee safety even while
running at nominal conditions. A 3D Convolutional Neural Network (3D-CNN) was
employed to analyse perturbations happening in the frequency domain, such as
the alteration of an absorber of variable strength or propagating perturbation.
Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) was
used to study signal sequences related to perturbations induced in the time
domain, including the vibrations of fuel assemblies and the fluctuation of
thermalhydraulic 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 frequency domain
related, a separate fully-connected layer utilises said representations to
regress the coordinates of its source. The results showed that perturbation
type can be recognised with high accuracy in both domains, and frequency domain
scenario sources can be localised with high precision.

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