  In this paper, we use topological data analysis techniques to construct a
suitable neural network classifier for the task of learning sensor signals of
entire power plants according to their reference designation system. We use
representations of persistence diagrams to derive necessary preprocessing steps
and visualize the large amounts of data. We derive deep architectures with
one-dimensional convolutional layers combined with stacked long short-term
memories as residual networks suitable for processing the persistence features.
We combine three separate sub-networks, obtaining as input the time series
itself and a representation of the persistent homology for the zeroth and first
dimension. We give a mathematical derivation for most of the used
hyper-parameters. For validation, numerical experiments were performed with
sensor data from four power plants of the same construction type.
