A new framework integrates JSBSim flight dynamics with LLMs to diagnose general aviation aircraft faults. Researchers used FMEA-driven injection to generate 23-channel engine health data, overcoming the scarcity of real-world failure samples. This multi-fidelity approach produces interpretable reports for technicians. It proves that synthetic digital twin data can effectively train diagnostic models for rare aviation failures.