An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation
This research addresses the lack of interpretability in black-box fault detection and diagnosis models, which is a critical issue for ensuring functional safety and reducing computational costs in real-time safety-critical automotive software systems.
This paper proposes an explainable hybrid 1D CNN-GRU deep learning model for fault detection, identification, and localization in automotive software systems (ASSs) validation. The model aims to provide clear understanding of prediction logic by integrating explainable AI techniques like IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, which facilitates root cause analysis and model adaptation.
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.