通过连续集成和智能故障分析,推进汽车软件系统的实时验证
Mohammad Abboush1, Christoph Knieke2, Andreas Rausch2
1Institute for Software and Systems Engineering, Technical University of Clausthal, 38678, Clausthal-Zellerfeld, Germany. mohammad.abboush@tu-clausthal.de.
Scientific reports
|September 25, 2025
概括
本研究介绍了一种支持连续集成的硬件循环测试框架和用于汽车软件验证的机器学习模型. 该方法增强了故障检测和诊断,提高了安全性和效率.
科学领域:
- 汽车软件工程 汽车软件工程
- 嵌入式系统测试 嵌入式系统测试
- 功能安全 (ISO 26,262) 标准是指功能安全.
背景情况:
- 汽车ECU的传统硬件在循环 (HIL) 测试发生在V型号的晚期,导致延迟反和不足的故障诊断.
- 手动分析测试记录阻碍了有效的故障识别,增加了开发成本和延迟检测.
- 目前的方法缺乏可扩展性,并阻碍了汽车软件开发中的敏捷协作.
研究的目的:
- 为代汽车软件开发提出一个新的持续集成 (CI) 支持的HIL测试框架.
- 为传感器相关故障开发智能,数据驱动的机器学习 (ML) 辅助故障检测和诊断 (FDD) 模型.
- 提高汽车软件验证工作流程中故障分析的准确性和效率.
主要方法:
- 实现一个支持CI的HIL测试框架,支持代软件开发周期.
- 开发 ML 辅助的 FDD 模型,包括用于分类的 LSTM 和用于集群的 K- 手段,使用关键故障数据集.
- 在分类和集群之前集成无噪自编码器 (DAE) 进行噪声强度特征提取.
主要成果:
- 与最先进的方法相比,拟议的模型在已知故障分类方面获得了91.85%的优异F1得分.
- DAE-K-means集成显示了对噪声的高集群性能,MSE (0.044) 和DBI (0.68) 低.
- 该方法使汽车软件验证中的故障分析更加有效,自动化和准确.
结论:
- 支持CI的HIL框架和ML辅助的FDD模型显著改善了汽车软件验证过程.
- DAE集成增强了故障检测和诊断的稳定性,特别是在噪音条件下.
- 与传统方法相比,这种方法可以提高安全性,效率和降低开发成本.
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