Reducing Technician Search Burden: A Multimodal RAG for Cessna 172 Maintenance Manual

Proper use of the aircraft maintenance manual is essential for correct maintenance, providing procedures, diagrams, cautions, and specifications. However, technicians often avoid consulting it because it is difficult to navigate and time-consuming under strict schedules. Retrieval augmented generation (RAG) models have recently been introduced in aircraft maintenance, yet existing models focus solely on textual retrieval. This research therefore targeted the Cessna 172 Maintenance Manual (C172-MM), widely used in general aviation, and developed a multimodal manual retriever (MMR) capable of retrieving multimodal manual pages. Retrieval performance was evaluated using synthetic queries covering procedures, diagrams, caution/safety information, and specifications; the MMR achieved 93.37% recall@5. Beyond retrieval, a multimodal RAG (MRAG) pipeline was examined, in which retrieved pages were input to a vision-language model that generated responses to the synthetic queries, achieving 87.20% semantic similarity to ground-truth answers. Three practical feasibilities were also assessed: inference time, operational cost, and interpretability. Average retrieval time for five pages was 11.93 seconds and response generation took 4.95 seconds, at $0.0091 per query, while interpretability was validated through heatmap visualizations. These results indicate that the MRAG pipeline for the C172-MM can reduce the time technicians spend searching manuals and retrieving multimodal information.