Problem
Research questions and friction points this paper is trying to address.
Robustness Validation
Automotive E/E Components
Mission Profiles
Manual Procedure
Complexity
Reasoning-supported Robustness Validation of Automotive E/E Components
🤖 AI Summary
This study addresses the complexity and error-proneness of robustness verification processes for automotive electrical and electronic components by proposing an OWL ontology-based semantic mapping method for mission profiles. By formally modeling component characteristics and mission profiles, we construct an automated verification framework that supports semantic querying to enable intelligent task selection and decision-making, effectively replacing traditional manual workflows. Applied to automotive power electronics, this approach significantly shortens design cycles while enhancing verification completeness and reliability. Ultimately, this work establishes a novel knowledge-driven paradigm for the verification of complex systems, demonstrating substantial improvements over conventional methodologies in both efficiency and accuracy.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Ontology-supported Validation
Mission Profiles
OWL Formalization
Semantic Queries
Automated Analysis Selection
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