一种可解释的混合深度学习智能故障检测与诊断方法用于汽车软件系统验证
An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation
AI总结：
本文提出了一种可解释的混合深度学习方法，用于汽车软件系统验证中的故障检测与诊断，通过可解释AI技术提升模型适应性和根本原因分析能力。
AI中文摘要：
数据驱动机器学习的进步已成为支持汽车软件系统（ASSs）工程在V开发过程各个层次中的关键要素。在系统验证和验证过程中，将智能故障检测与诊断（FDD）模型与测试记录分析过程整合起来，成为确保效率和功能安全的强大工具。然而，黑箱FDD模型的不可解释性不仅阻碍了对预测原因的理解，也阻止了模型基于预测结果的适应性调整。这反过来又增加了开发复杂FDD模型所需计算成本，并限制了实时安全关键应用的信心。为了解决这一挑战，本文提出了一种新的可解释方法用于故障检测、识别和定位，旨在提供预测结果背后逻辑的清晰理解。为此，开发了一种基于1dCNN-GRU的混合智能模型，用于分析ASSs实时验证过程的记录。可解释AI技术（即IGs、DeepLIFT、Gradient SHAP和DeepLIFT SHAP）的运用对于使模型适应和促进根本原因分析（RCA）至关重要。所提出的方法应用于用户在硬件在环系统中进行虚拟测试驾驶过程中收集的实时数据集。
英文摘要：
Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process. Duringsystemverificationandvalidation,theintegrationofanintelligent fault detection anddiagnosis (FDD) model with test recordings analysis process serves as a powerful tool for efficiency ensuring functional safety. However, the lack of interpretability of the black-box FDD models developed not only hinders understanding of the cause underlying the prediction, but also prevents the model from being adapted based on the prediction result. This, in turn, increases the computational cost required for developingacomplexFDDmodelandlimitsconfidenceinreal-timesafety-criticalapplications.To address this challenge, a novel explainable method for fault detection, identification, and localization is proposed in this article with the aim of providing a clear understanding of the logic behind the prediction outcome. To this end, a hybrid 1dCNN-GRU-based intelligent model was developed to analyze the recordings from the real-time validation process of ASSs. The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA). The proposed approach is applied to the real time dataset collected during a virtual test drive performed by the user on hardware in the loop system.
