ANOMALY DETECTION IN PRODUCTION PLANTS USING TIMED AUTOMATA - Automated Learning of Models from Observations

Abstract

Model-based approaches are used for testing and diagnosis of automation systems (e.g. (Struss and Ertl, 2009)). Usually the models are created manually by experts. This is a troublesome and protracted procedure. In this paper we present an approach to overcome these problems: Models are not created manually but learned automatically by observing the plant behavior. This approach is divided into two steps: First we learn the topology of automation components, the signals and logical submodules and the knowledge about parallel components. In a second step, a behavior model is learned for each component. Later on, anomalies are detected by comparing the observed system behavior with the behavior predicted by the learned model.