Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs
This work addresses the problem of interpretability in model-based anomaly detection for operators in cyber-physical production systems, representing an incremental improvement by combining existing formalisms.
The paper tackled the challenge of interpreting learned timed automata models and detected anomalies in Cyber-Physical Production Systems by integrating them with a formal knowledge graph, resulting in a validated approach that formally defined both the model and timing anomalies on a five-tank mixing system.
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.