TY - GEN
T1 - Automated Diagnosis of Cyber-Physical Systems
AU - Wotawa, Franz
AU - Tazl, Oliver
AU - Kaufmann, David
N1 - Funding Information:
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/.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Answer set programming
KW - Cyber-physical system diagnosis
KW - Model-based diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85112708377&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-79463-7_37
DO - 10.1007/978-3-030-79463-7_37
M3 - Conference paper
AN - SCOPUS:85112708377
SN - 9783030794620
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 441
EP - 452
BT - Advances and Trends in Artificial Intelligence. From Theory to Practice - 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Proceedings
A2 - Fujita, Hamido
A2 - Selamat, Ali
A2 - Lin, Jerry Chun-Wei
A2 - Ali, Moonis
PB - Springer Science and Business Media Deutschland GmbH
T2 - 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021
Y2 - 26 July 2021 through 29 July 2021
ER -