Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

Neural networks are being broadly explored for the identification of Industrial Cyber Physical Systems (ICPS) models from data sequences. However, learned representations typically lack explainability, representing a major challenge of deep learning. Interpreting the information structured across the synaptic links is particularly challenging for recurrent neural networks (RNN), encoding input features and observed system dynamics within a continuous latent space. In this work, we target the representations built within RNNs while learning behavioral models of a class of discrete dynamical systems. To this end, we propose a method to extract the symbolic knowledge structured by the continuous state, based on Gaussian Mixture Model clustering, handling latent activations characterized by partially overlapping and non-isotropic distributions. Experiments are performed on a pilot remanufacturing plant, by learning the model of a conveyor controller from process data. We show the capability of the proposed method to extract the hidden finite state machine from the trained RNN, providing a human interpretable representation of the input conditioned computations performed through the continuous latent space.