- 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.
- 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.
- T. Fabarisov, I. Mamaev, A. Morozov, and K. Janschek, “Deep Learning-based Error Mitigation for Assistive Exoskeleton with Computational-Resource-Limited Platform and Edge Tensor Processing Unit,” in The International Mechanical Engineering Congress and Exposition, IMECE, 2021.
- S. Ding, A. Morozov, T. Fabarisov, and S. Vock, “KrakenBox: Deep Learning based Error Detector for Industrial Cyber-Physical Systems,” in The International Mechanical Engineering Congress and Exposition, IMECE, 2021.
- A. M. T. Fabarisov, I. Mamaev and K. Janschek, “Model-based Fault Injection Experiments for the Safety Analysis of Exoskeleton System,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, 2020.
- T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems,” Acta Polytechnica Hungarica, 17(8), pp.15-28, 2020.
- T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “Analytical toolset for model-based stochastic error propagation analysis: extension and optimization towards industrial requirements,” Системная инженерия и информационные технологии, 1(1), pp.41-46, 2019.
- K. Ding, S. Ding, A. Morozov, T. Fabarisov, and K. Janschek, “On-line error detection and mitigation for time-series data of cyber-physical systems using deep learning based methods,” in 2019 15th European Dependable Computing Conference (EDCC) (pp. 7-14). IEEE, 2019.
- T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “The efficiency comparison of the prism and storm probabilistic model checkers for error propagation analysis tasks,” Industry 4.0, 3(5), pp.229-231., 2018.
Tagir Fabarisov is a PhD Student and Research Associate at the University of Stuttgart, Institute of Industrial Automation and Software Engineering. His main field of research is dependability, reliability and safety properties of Cyber-Physical Systems. As well as, the modern approaches for increasing of resilience of said systems, including Deep Learning-based methods. Another focus of research is Model-based approaches for dependability and safety analysis. Particularly, model-based Fault Injection experiments for a system reliability evaluation.
Cyber-Physical Systems (CPS) consists of various types components, such as sensors, computing, and network hardware, software, actuators, and physical parts are prone to various fault types. This heterogeneity leads to inability of the conventional anomaly detection techniques employment without the target system becoming overcomplicated. Thus, the Deep Learning-based anomaly techniques are getting more attention in this regard. They encompass good performance and flexibility by learning to represent the data as a nested hierarchy of concepts within layers of the neural network. Deep learning outperforms the traditional machine learning as the scale of data increases. That is indeed the case when dealing with the large amounts of the time-series signal generated by the various CPS component. However, the main difficulties are that the multi-class supervised techniques require labelled training sets that would consists of different and numerous erroneous data. Since correct data instances are more common, it is a challenging task to obtain the required amount of the accurately labelled training sets for all error types. For that, the Model-based Fault Injection techniques are being investigate and are subject of current research and development. With effective Fault Injection experiments, the erroneous data for Neural Networks could be generated as well as, it could be used for further testing of their error detection and mitigation capabilities.
Personal web-page: https://flatag.tech/
Google Scholar: https://scholar.google.com/citations?user=Ldmk7kIAAAAJ