Dynamic knowledge graph enhanced large language model with cascade relation extraction optimized for aviation equipment fault diagnosis

This paper develops a dynamic Knowledge Graph (KG)-augmented Large Language Model (LLM) framework integrated with a Bidirectional Encoder Representations from Transformers-Cascade Relation Extraction (BERT-CasRel) architecture to address key challenges in aviation equipment fault diagnosis, including unstructured maintenance text processing, ambiguous domain semantics, static knowledge constraints, and limited explainable reasoning capabilities. The study first constructs a domain-specific aviation ontology and adopts a context-enhanced BERT-CasRel model to extract high-quality entity–relation triples from maintenance logs and technical documentation. These structured triples populate a dynamic aviation fault KG that supports hierarchical causal inference, subgraph refinement, and in-context learning for adaptive knowledge updating. Structured domain prompting enables bidirectional interaction between LLMs and the KG, facilitating traceable fault chain analysis and accurate root-cause diagnosis. Evaluated on CFM56-5 aero-engine turbine blade fault cases, the BERT-CasRel model achieves a triple extraction F1-score of 0.968, while the integrated LLM–KG framework attains fault diagnosis accuracy exceeding 95%. Benchmarking against conventional and state-of-the-art methods confirms the framework's superiority in extraction accuracy, diagnostic precision, interpretability, and scalability. It delivers strong cross-domain generalization and computational efficiency, mitigates LLM hallucinations, complies with aviation regulations, and provides an interpretable, scalable diagnostic solution while acknowledging limitations in large-scale knowledge iteration and full industrial deployment.