Learning parallel automata of PLCs

Abstract

A large part of the programmable logic controls (PLCs) used in industrial automation systems is based on automata, which are employed to model the different stages of the automated processes and to determine the discrete control signals. Complex PLCs are typically composed of several parallel automata, which are related to a subset of the IO signals, respectively. In this paper, a novel model learning approach is proposed, which allows to learn the parallel automata from the discrete IO signals during normal operation of the PLC. Learning the parallel automata is accomplished by means of a synchronous side-by-side decomposition of the overall system model. The side-by-side decomposition is based on the clustering of the correlation matrix computed between the individual IO signals. The learnt automata can be employed for automatic fault detection and visualization of the normal operation of the PLC. Evaluations are conducted for both a baseline method, where a single automaton is learned as model for the complete system, and the proposed learning algorithm for parallel automata. Experimental results show that the computed parallel automata are superior to a single automaton with respect to compactness, accuracy and fault detection capabilities.