Authors:
Alexander Maier
1
;
Oliver Niggemann
1
;
Roman Just
1
;
Michael Jäger
1
and
Asmir Vodenčarević
2
Affiliations:
1
OWL Universitiy of Applied Sciences, Germany
;
2
University of Paderborn, Germany
Keyword(s):
Parallelism structure, Behavior model, Timed automata, Anomaly detection, Model-based diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Discrete Event Systems
;
Informatics in Control, Automation and Robotics
;
Instrumentation Networks and Software
;
Signal Processing, Sensors, Systems Modeling and Control
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.