Development and Validation of an Explainable Hybrid Deep Learning Model for Multiple-Fault Diagnosis in Intelligent Automotive Electronic Systems

This study addresses the increasingly complex challenge of multiple-fault diagnosis in modern intelligent automotive electronic systems by proposing an innovative deep learning-based solution. The research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the Transformer architecture to construct a multi-modal fault diagnosis model. By collecting real-world operational data from vehicle electronic systems, including fault samples from key modules such as the Engine Control Unit (ECU), Body Control Module (BCM), and safety systems, a comprehensive dataset comprising 12 major fault types was established. Experimental results demonstrate that the proposed hybrid deep learning model achieves a multiple-fault identification accuracy of 96.8%, representing a 23% performance improvement over traditional diagnostic methods. The integration of Explainable AI (XAI) techniques provides the diagnostic results with visual interpretability, aiding maintenance technicians in understanding the model’s diagnostic logic. The findings of this research can be applied in smart factories, automotive service centers, and on-board diagnostic (OBD) systems, offering significant practical value in enhancing vehicle safety and reducing maintenance costs.