A Knowledge Graph - RAG based Troubleshooting Guide for Helicopter Systems

Abstract:
Helicopter maintenance troubleshooting faces significant challenges due to fragmented documentation, outdated procedural manuals, and reliance on human expertise, all of which threaten flight safety and operational efficiency. While Knowledge Graphs (KGs) effectively model hierarchical system relationships and causal dependencies, they struggle with dynamic unstructured data. Conversely, Retrieval-Augmented Generation (RAG) systems access technical manuals but risk hallucinating unsafe procedures without structural grounding. This paper introduces KG-RAG, a novel hybrid troubleshooting framework specifically engineered for helicopter systems, addressing a critical gap as existing work focuses predominantly on fixed-wing aircraft. The framework merges knowledge graphs modeling fault causality and maintenance history with multi-dimensional retrieval combining graph-based reasoning, vector embeddings, and keyword-based search. This integration enables contextual interpretation of ambiguous fault descriptions, generation of precise diagnostics aligned with operational constraints, and dynamic adaptation to new fault patterns without retraining. By transforming fragmented maintenance knowledge into a verifiable, context-aware troubleshooting guide, the framework directly addresses aviation's persistent obstacles: data incompleteness, knowledge erosion, and slow safety-critical decisions. This work positions KG-RAG not merely as a tool but as a foundational shift toward cognitively augmented maintenance, elevating human expertise through AI that reasons like an engineer and contextualizes like a veteran technician for enhanced safety-critical decision-making in complex helicopter operations.

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