Modeling Cyber-Physical Systems (CPS) requires knowledge from various domains, including computer science, electrical and mechanical engineering, and control theory. One conventional approach to model CPS is to describe the physical relationships as a classical system of formulas. This requires, a solid understanding of the application domain is required to ensure relevant and accurate models. The behavior of a CPS can be estimated by numerical simulations using e.g. Matlab, Simulink, or Modelica. However, these approaches require that the system’s internals are sufficiently and accurately known, which often is not the case CPS. Thus we employ data driven learning approaches to automatically generate CPS models. We develop Flowcean, which offers a toolbox for modeling CPS from various industrial domains [1]. The project’s consortium spans three distinct application domains, maritime systems, energy grids, and intralogistics, represented by industrial partners KALP GmbH, VIVAVIS AG, SICK AG and KION GROUP AG. The company KALP deploys self-sufficient automated twistlock handling platform based on a hydraulic pressure system. VIVAVIS provides smart IoT solutions especially for the efficient control of smart electricity grids [2]. Associated partners bring further expertise in sensor technology (SICK) and robotics (KION) for intralogistic scenarios [3]. All partners provide either software or hardware solutions to demonstrate how Flowcean can improve testing, operation, and monitoring of CPS.
Flowcean applies data driven learning to understand and replicate the individual system behavior. The modeling process is structured into four steps:
1. Loading recorded data or starting a simulation
2. Transforming data to a suitable format
3. Learning models using data driven techniques
4. Evaluating the model’s performance via various metrics
Flowcean encompasses both online and offline learning techniques [4],[5]. To be able to model CPS of various domains, the entire pipeline has a modular structure. Thus, each transforming or learning step is composable with others to create distinct pipelines.
So far, the design of the framework’s architecture and the implementation of basic examples prove a functioning application. Our upcoming goals are
• the analysis of more complex CPS from the domains of the project’s consortium,
• finalizing the integration of online and offline learning strategies as well as
• the development of tools to use learned models for testing [6], monitoring, or behavioral prediction.
Partners:
• Fraunhofer Center for Maritime Logistics and Services (CML)
• Institute of Embedded Systems, Hamburg University of Technology
• Institute of Technical Logistics, Hamburg University of Technology
• OFFIS – Institut für Informatik
• VIVAVIS AG
• KALP GmbH
• SICK AG
• KION GROUP AG
The project is funded by the Federal Ministry of Education and Research (BMBF).
Contact:
Hendrik Rose & Markus Knitt
Institute for Technical Logistics
hendrik.wilhelm.rose@tuhh.de, markus.knitt@tuhh.de
Maximilian Schmidt & Swantje Plambeck & Görschwin Fey
Institute of Embedded Systems
maximilian.schmidt@tuhh.de, swantje.plambeck@tuhh.de, goerschwin.fey@tuhh.de
Bibliography
[1] M. Knitt et al., “Towards the Automatic Generation of Models for Prediction, Monitoring, and Testing of Cyber-Physical Systems”, in International Conference on Emerging Technologies and Factory Automation (ETFA), 2023.
[2] L. Fischer, J.-M. Memmen, E. M. S. P. Veith, and M. Tröschel, “Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems”, ArXiv, 2018.
[3] M. Knitt, Y. Elgouhary, J. Schyga, H. Rose, P. Braun, and J. Kreutzfeldt, “Benchmarking for the Indoor Localization of Autonomous Mobile Robots in Intralogistics”, Logistics Journal : Proceedings, no. 1, 2023.
[4] J. Schyga, S. Plambeck, J. Hinckeldeyn, G. Fey, and J. Kreutzfeldt, “Decision Trees for Analyzing Influences on the Accuracy of Indoor Localization Systems”, in International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2022.
[5] E. Veith et al., “palaestrAI: A Training Ground for Autonomous Agents”, in European Simulation and Modelling Conference (ESM), 2023.
[6] S. Plambeck and G. Fey, “Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree Models”, in International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS), 2023.
