An Automotive Fault Diagnosis Framework Based on Knowledge Graphs and Large Language Models

In recent years, the rapid advancement of large language models (LLMs) has driven significant breakthroughs in artificial intelligence. Leveraging LLMs in conjunction with domain-specific knowledge to develop intelligent assistants can reduce operational costs and facilitate industrial upgrading. In the field of automotive fault diagnosis, traditional methods rely heavily on technicians’ experience, resulting in limitations in both efficiency and accuracy. Misdiagnosis or insufficient expertise can lead to repair delays, while information asymmetry may cause trust issues between service providers and customers. To address these challenges, we propose a vehicle fault diagnosis framework based on knowledge graphs and large language models. Unlike traditional retrieval-augmented generation (RAG) methods, our framework actively queries for missing information and delivers precise repair recommendations. Experimental evaluations demonstrate that our framework achieves a diagnosis accuracy of 77.3%, representing a 46.1% improvement over direct diagnosis using a pretrained LLM (GPT-3.5) and over a 14% increase compared to other existing frameworks. Ablation studies confirm the effectiveness of each module, and our findings are further illustrated through detailed charts and visualizations. Overall, this study highlights the potential of integrating knowledge graphs with large language models for automotive fault diagnosis, with promising applicability to other traditional industries.