Hybrid online timed automaton learning algorithm for discrete manufacturing systems

Abstract

This paper presents the Hybrid Online Timed Automaton Learning Algorithm (HyOTALA), a novel approach for anomaly detection in cyber-physical production systems (CPPS). It addresses the challenge of using hybrid data, combining sparse discrete and continuous signals, by learning a hybrid timed automaton model in an online setting. This model captures the dynamics of discrete manufacturing processes and provides significant advances in model identification for CPPS. The effectiveness of HyOTALA is demonstrated through a practical application, highlighting its potential to improve anomaly detection capabilities in industrial settings.