用知识图谱自动选测试，让汽车电子部件可靠性验证更快更全。
Reasoning-supported Robustness Validation of Automotive E/E Components
- 用OWL和SWRL构建组件与工况的语义模型，实现自动化分析选择。
- 实验显示设计时间减少，测试覆盖更全面，关键数据自动提供。
- 适合汽车电子可靠性验证团队，尤其关注流程自动化者。
本文提出一种基于本体的可靠性验证（RV）方法，应对汽车电电子（E/E）组件验证过程的复杂性。该方法利用形式化知识，包括应力、运行及负载特性等任务剖面（MPs）。与传统易出错的手动流程相比，本文展示如何将组件特性形式化为OWL知识，并用于支持自动化分析选择与决策。同时，通过SWRL规则实现组件特性在传播中的转换。核心思想是将任务剖面映射为OWL表示，从而支持对任务剖面数据的语义查询，提升其在验证流程中的集成能力。所提出的本体支持应用框架已应用于汽车功率电子领域的工业案例。进一步通过AEC Q100标准下的应力测试选择，展示了该方法的可泛化性。实验结果表明，通过自动化分析选择与相关数据的自动供给，可靠性验证流程在设计时间上显著缩短，测试覆盖范围显著提升。
原文摘要 · Abstract (English)
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.
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