Abstract
System monitoring is essential for detecting failures during operation and ensuring reliability. A monitoring system obtains observations and checks their consistency concerning requirements formalized as properties. However, finding property violations does not necessarily mean finding the causes. In this paper, we contribute to the latter and suggest introducing model-based diagnosis for root cause identification. We do this by adding information regarding the source of observations. Furthermore, we suggest implementing properties using ordinary programming languages from which we can obtain a formal model directly. Finally, we explain the process of integrating diagnosis into monitoring and show its value using a case study from the automotive domain.
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Notes
- 1.
In ordinary MBD, we would directly write this property on the right side of the implication \(\lnot ab(AB) \rightarrow ...\). In monitoring, however, we may have different signals originating from a component and different properties to be fulfilled. The coding of the described knowledge allows for separating the originator of signal values from stated properties.
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Acknowledgements
ArchitectECA2030 receives funding within the Electronic Components and Systems For European Leadership Joint Undertaking (ESCEL JU) in collaboration with the European Union’s Horizon2020 Framework Programme and National Authorities, under grant agreement number 877539. All ArchitectECA2030 related communication reflects only the author’s view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. The work was partially funded by the Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) under the program “ICT of the Future” project 877587.
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Wotawa, F. (2023). Which Components to Blame? Integrating Diagnosis into Monitoring of Technical Systems. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13926. Springer, Cham. https://doi.org/10.1007/978-3-031-36822-6_3
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