AI 中文总结
该研究提出KG引导的RAG-LLM框架，用于汽车HiL数据的RCA与故障定位，在ASM汽油发动机和电动车系统案例中分别获90%、94% Top-1准确率，可实现可解释且泛化的故障分析。
AI 中文摘要
汽车软件系统的在环（Hardware-in-the-Loop，HiL）验证会产生大量多元时间序列记录，其手动分析耗时且通常仅局限于异常检测和故障分类，而非根本原因分析。尽管深度学习方法在故障检测与分类中表现出色，但当引入新的故障位置、系统或运行条件时，它们通常需要特定任务的训练或重新训练；且它们往往将定位视为分类任务，未明确表示故障位置、传感器与下游子系统效应之间的空间和功能关系，这限制了其泛化能力及对工程根本原因分析（RCA）与诊断的实用性。本文提出一种知识图谱（KG）引导的检索增强大语言模型（RAG-LLM）框架，用于汽车HiL数据的根本原因分析与故障定位。该方法将原始时间序列记录转换为紧凑的诊断证据，用传感器-位置及传播知识丰富该证据，并检索相似历史案例以支持最终推理步骤；随后将大语言模型用作决策与解释层，而非直接的时间序列分类器，生成排名式故障位置预测及可解释的根本原因分析解释。该框架在两个汽车HiL案例研究中评估：ASM汽油发动机与电动汽车系统，表现最佳的模型分别达到Top-1准确率90%和94%，而记录级聚合在评估子集上实现了完美的文件级故障定位。这些结果证明了知识图谱引导的检索增强大语言模型推理在可解释且泛化的汽车在环根本原因分析中的潜力。
英文摘要
Hardware-in-the-Loop validation of automotive software systems generates large multivariate time-series recordings whose manual analysis is time-consuming and often limited to anomaly detection and fault classification rather than root-cause analysis. Although deep learning methods have shown strong performance in fault detection and classification, they usually require task-specific training or retraining when new fault locations, systems, or operating conditions are introduced. They also tend to treat localization as a classification task, without explicitly representing the spatial and functional relationships between fault locations, sensors, and downstream subsystem effects. This limits their generalizability and their usefulness for engineering root cause analysis and diagnosis. This paper proposes a knowledge-graph-guided retrieval-augmented large language model framework for RCA (root cause analysis) and fault localization in automotive HiL data. The method converts raw time-series recordings into compact diagnostic evidence, enriches this evidence with sensor-to-location and propagation knowledge, and retrieves similar historical cases to support the final reasoning step. The LLM is then used as a decision and explanation layer rather than as a direct time-series classifier, producing a ranked fault-location prediction together with an interpretable RCA explanation. The framework is evaluated on two automotive HiL case studies: an ASM gasoline engine and an electric vehicle system. The best-performing model achieves Top-1 accuracies of 90\% and 94\%, respectively, while recording-level aggregation reaches perfect file-level fault localization in the evaluated subset. These results demonstrate the potential of KG-guided RAG-LLM reasoning for explainable and generalizable HiL RCA.
Comments10 pages, 3 figures, 5 tables. Accepted for publication and oral presentation at the 10th International Conference on System Reliability and Safety (ICSRS 2026), Rome, Italy, November 23--25, 2026
