基于推理的汽车电子电气组件鲁棒性验证
Reasoning-supported Robustness Validation of Automotive E/E Components
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中文总结 AI 辅助
本文提出一种本体支持的方法，将任务剖面（MP）映射到OWL表示，应用于汽车电力电子工业用例，可显著缩短汽车电子电气组件鲁棒性验证的设计时间并提高其完备性。
中文摘要 AI 辅助
本文提出一种本体支持的方法，以解决汽车电子电气（E/E）组件鲁棒性验证（RV）流程的复杂性问题。该方法利用RV流程以及应力、运行和负载剖面（即所谓的任务剖面（MPs））的形式化知识。与工业界易出错的既定手动流程不同，我们展示了如何用OWL形式化组件特性，从而为RV流程中高效的自动化分析选择和决策支持奠定基础。所提出的方法基于将MP映射到OWL表示的思路，以便对MP数据执行语义查询，以改进其与RV流程的集成。生成的本体支持的应用框架已应用于汽车电力电子的工业用例。我们呈现的实验结果表明，通过自动化分析选择步骤以及提供所有要使用的相关数据，RV流程在减少设计时间和提高完备性方面可以得到显著改善。
英文摘要
This article 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. Additionally, a rule-based transformation of component characteristics upon propagation via SWRL is described. The proposed approach is based on the idea of mapping MPs to an OWL representation in order to allow to execute 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. A generalization of the approach is described and demonstrated by applying it to stress test selection within the AEC Q100 standard. 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.
发表机构
- FZI Forschungszentrum Informatik（FZI信息技术研究中心）
- Eberhard Karls Universität Tübingen（蒂宾根大学）
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