面向汽车软件系统实时验证中基于AI的故障分析的代表性数据集生成框架
Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems
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中文总结 AI 辅助
本研究提出基于HIL仿真与实时FI方法的框架，生成汽车软件系统实时验证中ML应用的代表性故障数据，可生成序列与文本数据，能有效模拟捕获故障下的系统行为。
中文摘要 AI 辅助
近年来，得益于从历史数据集中提取知识的能力，数据驱动方法已广泛应用于系统开发生命周期的各个阶段。在实时系统验证期间，基于历史数据的智能故障分析已取得显著成果。然而，尽管其优于其他传统方法（如基于模型和基于信号的方法），代表性数据集的可用性仍是一个主要挑战。因此，针对不同工程应用，需探索生成不同形式的代表性故障数据的新方案。本研究提出一种基于硬件在环（HIL）仿真和实时故障注入（FI）方法的新方法，用于在系统验证阶段生成和收集机器学习（ML）应用的单故障及同时故障下的数据样本。所开发的框架不仅能生成序列数据，还能生成包括故障日志在内的文本数据。结果表明，该框架可有效模拟并捕获系统组件在故障下的系统行为，具有适用性。
英文摘要
Recently, thanks to its ability to extract knowledge from historical datasets, the data-driven approach has been widely used in various phases of the system development life cycle. During real-time system validation, remarkable achievements have been accomplished in developing an intelligent failure analysis based on historical data. However, despite its superiority over other conventional approaches, e.g., model-based and signal-based, the availability of representative datasets persists as a major challenge. Thus, for different engineering applications, new solutions to generate representative faulty data in different forms should be explored. Therefore, in this study, a novel approach based on Hardware-in-the-Loop (HIL) simulation and real-time Fault Injection (FI) method is proposed to generate and collect data samples under single and simultaneous faults for Machine Learning (ML) applications during system validation phases. The developed framework can generate not only sequential data, but also textual data including fault logs. The results show the applicability of the proposed framework in simulating and capturing the system behaviour under faults within the system components.
