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

作者：Auwal Haruna, Li Li, Li Li, Khandaker Noman, Yongbo Li, Yongbo Li, Fatin Abrar Shams · 发表于：Engineering Applications of Artificial Intelligence · 年份：2026 · DOI：10.1016/j.engappai.2026.115762 · 被引用次数：1 · 研究领域：Advanced Graph Neural Networks、Machine Fault Diagnosis Techniques、Topic Modeling

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 h...