Towards the Automatic Generation of Models for Prediction, Monitoring, and Testing of Cyber-Physical Systems

Modeling Cyber-Physical Systems (CPS) requires knowledge from various domains, including computer science, electrical and mechanical engineering, and control theory. In addition, a solid understanding of the application domain, e.g., intralogistics, maritime technology, or grid control technology is required to ensure relevant and accurate models. In order to reduce the knowledge required for modeling CPS, we envision a framework for Automatic Generation of models for CPS (AGenC) supporting design and operation. Typical tasks in design and operation are summarized by the terms prediction, monitoring, and testing. Thus, the proposed framework employs learning techniques to generate models of CPS that predict the system’s performance in various scenarios, monitor the system in real-time to detect anomalies or failures, and automatically generate test cases. This research has the potential to significantly reduce the time and effort required for designing, testing, and maintaining CPS, making them more reliable and efficient.