Reasoning-supported Robustness Validation of Automotive E/E Components

Abstract:This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. The proposed approach is based on the idea of mapping MPs to an OWL representation so to allow to perform semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.

Artificial Intelligence (cs.AI)

arXiv:2608.16421 [cs.AI]

(or arXiv:2608.16421v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.16421 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 2017, pp. 220-226

https://doi.org/10.1109/icsc.2017.28 Focus to learn more DOI(s) linking to related resources

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