Aircraft Fault Knowledge Graph Construction Based On Large Language Model Incorporating Chinese Airworthiness Knowledge

Airworthiness directives contain rich, standardized information critical for diagnosing aircraft faults. However, the complexity, domain specificity, and heterogeneous data characteristics of these texts make it difficult to extract and structure this knowledge into an organized fault knowledge graph (KG), thereby limiting progress toward intelligent civil-aviation maintenance and management. To address this challenge, we propose a large language model (LLM) fine-tuning approach that integrates domain knowledge to mine fault knowledge from Chinese Airworthiness Directive (CAD) texts. After comprehensive text preprocessing and expert-guided manual annotation, we constructed a specialized dataset for aircraft fault knowledge discovery, encompassing named entity recognition (NER) and relation extraction (RE) tasks. The LLM was fine-tuned through parameter-efficient adaptation methods (Freeze, P-tuning, and LoRA), with domain knowledge incorporated via tailored prompt templates to enable intelligent knowledge extraction from CAD texts. Experimental results demonstrate that the domain-enhanced LLM achieves F1 scores of 81.64% on NER and 88.30% on RE -- improvements of 12.76% and 3.97%, respectively, over conventional pretrained language models (PLMs). These results confirm the effectiveness of the proposed knowledge-embedded LLM framework in constructing aircraft fault KGs and advancing expert systems for civil-aviation safety and airworthiness management.