Aircraft maintenance logs hold valuable safety data but remain underused due to their unstructured text format. This paper introduces LogSyn, a framework that uses Large Language Models (LLMs) to convert these logs into structured, machine-readable data. Using few-shot in-context learning on 6,169 records, LogSyn performs Controlled Abstraction Generation (CAG) to summarize problem-resolution narratives and classify events within a detailed hierarchical ontology. The framework identifies key failure patterns, offering a scalable method for semantic structuring and actionable insight extraction from maintenance logs. This work provides a practical path to improve maintenance workflows and predictive analytics in aviation and related industries.

翻译：飞机维护日志包含宝贵的安全数据，但由于其非结构化文本格式而未能得到充分利用。本文介绍了LogSyn，一个利用大语言模型将这些日志转换为结构化、机器可读数据的框架。通过对6,169条记录进行少样本上下文学习，LogSyn执行受控抽象生成，以总结问题-解决叙述，并在一个详细的分层本体中对事件进行分类。该框架识别关键故障模式，为从维护日志中进行语义结构化和可操作见解提取提供了一种可扩展的方法。这项工作为改进航空及相关行业的维护工作流程和预测性分析提供了一条实用路径。