An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation

2026-02-01

Knowledge-Based Systems

AI Summary

This study addresses the limitations of traditional black-box fault diagnosis models, which lack interpretability and thus fail to meet the stringent demands for trustworthiness and efficiency in real-time, safety-critical automotive software systems. To overcome this, the authors propose a hybrid deep learning architecture that integrates one-dimensional convolutional neural networks (1D CNN) with gated recurrent units (GRU). For the first time, multiple explainable AI (XAI) techniques—including Integrated Gradients, DeepLIFT, and Gradient SHAP—are systematically incorporated into this framework to enable high-accuracy fault detection, identification, and localization. Experimental results on real hardware-in-the-loop virtual driving test data demonstrate that the proposed approach not only significantly enhances diagnostic transparency but also effectively supports root cause analysis and model self-adaptation, offering a practical and interpretable solution for real-time fault diagnosis in safety-critical systems.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Hardware-aware ML
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• fault detection and diagnosis
• explainability
• automotive software systems
• black-box models
• functional safety

Innovation

• Explainable AI
• Hybrid Deep Learning
• Fault Detection and Diagnosis
• 1D CNN-GRU
• Root Cause Analysis