  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 (3D-CNN) 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.
