Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

TL;DR

This paper enhances model-based anomaly detection in Cyber-Physical Production Systems by integrating learned timed automata with a formal knowledge graph, improving interpretability of models and anomalies.

Contribution

It introduces a method to combine timed automata with knowledge graphs and proposes an ontology for system concepts, aiding interpretation.

Findings

Validated on a five-tank mixing CPPS

Successfully defined automata and anomalies in the knowledge graph

Improved interpretability of models and detected anomalies

Abstract

Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.