LogSyn: A Few-Shot LLM Framework for Structured Insight Extraction from Unstructured General Aviation Maintenance Logs

2025-11-23

AI Summary

Aviation maintenance logs contain critical safety information, yet their unstructured nature severely hinders automated analysis and knowledge mining. Method: This paper proposes a few-shot log structuring method for general aviation maintenance logs, built upon a fine-grained hierarchical event ontology and integrating large language models (LLMs) via in-context learning and controlled abstract generation (CAG) to jointly perform semantic parsing, failure mode identification, and maintenance knowledge extraction. Contribution/Results: The method achieves high-accuracy log structuring with only minimal annotated examples. Evaluated on 6,169 real-world logs, it significantly enhances machine readability and analytical efficiency. Its core innovation lies in synergistically coupling domain-specific ontological guidance with controllable LLM generation—overcoming the technical bottleneck of deep understanding of unstructured operational text under few-shot constraints. This work establishes a novel paradigm for predictive maintenance and intelligent aviation safety analytics.

Technology Category

• Machine Learning: Structured Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Extracting structured insights from unstructured aviation maintenance logs
• Converting maintenance narratives into machine-readable data using LLMs
• Identifying failure patterns through semantic structuring of maintenance records

Innovation

• Uses few-shot learning for log abstraction
• Applies hierarchical ontology for event classification
• Converts unstructured logs into structured data