铁路通信信号工程技术2025，Vol.22Issue(7)：18-26,87,10.DOI:10.3969/j.issn.1673-4440.2025.07.003
铁路信号故障分析领域大语言模型微调方法
Large Language Model Fine-tuning Method for Railway Signaling Fault Analysis Domain
摘要
Abstract
To enhance the intelligent analysis of railway signaling equipment failures,this study proposes a domain-specific large language model fine-tuning approach established for the failure diagnosis task of railway signaling equipment.The proposed method employs Low-Rank Adaptation(LoRA)technology to achieve parameter-efficient fine-tuning,significantly reducing training costs.The topological Chain-of-Thought(CoT)technology is integrated to establish the causal reasoning framework for fault diagnosis,improving the model's logical interpretability.By incorporating an external fault knowledge base with retrieval-augmented mechanisms,the proposed system demonstrates enhanced terminology recognition and knowledge adaptation capabilities.Experimental results indicate that this approach achieves an 11.2%accuracy improvement in faulty board information extraction compared to baseline models,while reducing inference time by 52%.When the reasoning time is comparable,the accuracy of this method is 2.5%higher compared with that of the traditional fine-tuning method.In cross-domain migration scenarios,the proposed model exhibits robust generalization capabilities with a 42.6%accuracy enhancement.Through technical integration and knowledge enhancement,this method effectively addresses the efficiency and generalization bottlenecks inherent in conventional approaches,substantially improving diagnostic accuracy,operational efficiency,and domain adaptability.The proposed system provides critical technical support for intelligent railway signaling maintenance,accelerating the practical implementation of AI technologies in rail transportation scenarios.This research represents significant innovation with prominent application value.关键词
人工智能/大语言模型微调/思维链/知识检索增强/铁路信号Key words
artificial intelligence/large language model fine-tuning/chain-of-thought/retrieval-augmented knowledge enhancement/railway signaling分类
交通工程引用本文复制引用
孙超,李涵蕊,丁子焕..铁路信号故障分析领域大语言模型微调方法[J].铁路通信信号工程技术,2025,22(7):18-26,87,10.基金项目
中国国家铁路集团有限公司科技研究开发计划重点课题项目(N2023G081) （N2023G081）
