Abstract
Research on cyber-physical systems has gained importance and we see an increasing number of applications ranging from ordinary cars to autonomous systems. The latter are of increasing interest requiring additional functionality like self-healing capabilities for improving availability. For autonomous systems, it is not only important to detect failures during operation, but also to come up with their causes. In this paper, we contribute to the foundations of diagnosis. We introduce a method for modeling cyber-physical systems considering behavior over time, in order to make use of model-based reasoning for computing diagnosis candidates. In particular, we discuss a thermal model coupled with a controller for keeping temperature within pre-defined values and show how this contributes to the computation of diagnoses given an unexpected behavior. The discussed modeling principles can be used as a blueprint for similar systems where controllers are coupled with a physical system. Diagnosis results obtained when using the thermal model and the observed diagnosis time, which was a fraction of a second, seem to indicate the applicability of the presented approach for industrial applications.
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Notes
- 1.
We used a MacBook Pro (15-inch, 2016), 2.9 GHz Quad-Core Intel Core i7, with 16 GB 2133 MHz LPDDR3 ram, and macOS Big Sur Version 11.1 for carrying out the experiments.
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Acknowledgement
The research was supported by ECSEL JU under the project H2020 826060 AI4DI - Artificial Intelligence for Digitising Industry. AI4DI is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under the program “ICT of the Future” between May 2019 and April 2022. More information can be retrieved from https://iktderzukunft.at/en/.
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Wotawa, F., Tazl, O., Kaufmann, D. (2021). Automated Diagnosis of Cyber-Physical Systems. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_37
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