Contact
+49 711 685 67319
+49 711 685 67302
Email
Pfaffenwaldring 47
70550 Stuttgart
Germany
Room: 1.302
2022
- P. Grimmeisen, M. Diaconeasa, Y. Ma, and A. Morozov, “Automated Generation of Hybrid Probabilistic Risk Models from SysML v2 Models of Software-Defined Manufacturing Systems,” in Morozov ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
- P. Grimmeisen, A. Morozov, T. Fabarisov, A. Wortmann, and C. H. Koo, “Automated Model-Based Reliability Assessment of Software-Defined Manufacturing,” in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
- P. Grimmeisen, A. Wortmann, and A. Morozov, “Case study on automated and continuous reliability assessment of software-defined manufacturing based on digital twins,” MODELS ’22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, 2022.
- Y. Ma, P. Grimmeisen, and A. Morozov, “Detection and classification of robotic manipulator anomalies using MLSTM-FCN models,” ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
- T. Fabarisov, A. Morozov, I. Mamaev, and P. Grimmeisen, “FIDGET: Deep Learning-Based Fault Injection Framework for Safety Analysis and Intelligent Generation of Labeled Training Data,” 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
Research focus:
Automated Risk Analysis of Software-defined Systems/Robots
Description:
Software-defined systems require a new approach to risk analysis. These systems are characterized by features such as frequent SW updates, AI components, and the use of digital twins, and must be analyzed for reliability and dependability. With each SW update, the behavior of the system can change, thus the risk analysis must be performed in an automated way before each SW update.
The automated generation of advanced hybrid risk models and model-to-model transformation methods are crucial for this purpose. The integration and synchronization of the risk analysis module with the digital twin shall be investigated. AI-based methods are used to extract the required inputs for the risk models from the digital twin. A combination of formal methods and fault injection will be investigated to assess the risk of AI-based SW components. The developed risk models will be able to adapt to the behavior of mutable systems, in contrast to classical risk models.
Research portal:
ResearchGate: https://www.researchgate.net/profile/Philipp-Grimmeisen
LinkedIn: https://www.linkedin.com/in/philipp-grimmeisen-23a080223/