LLM-Enhanced Aircraft Piston Engine Diagnostics with Expert Knowledge Integration

This paper presents a novel approach to aircraft piston engine diagnostics utilizing a Large Language Model (LLM) enhanced with domain-specific knowledge. The methodology combines traditional engine monitoring data analysis with advanced natural language processing capabilities to identify and diagnose engine anomalies. The system processes real-time engine parameters including Cylinder Head Temperatures (CHTs), Exhaust Gas Temperatures (EGTs), engine RPM, Fuel Flow (FF), and Turbine Inlet Temperature (TIT) from engine monitoring systems. The LLM's diagnostic capabilities are augmented through the integration of expert knowledge, implemented as a comprehensive set of IF-THEN rules and detailed textual descriptions of common engine fault patterns extracted from maintenance documentation. This hybrid approach enables the system to leverage both structured operational data and unstructured maintenance expertise to provide accurate fault diagnostics. The methodology shows particular promise in detecting subtle fault patterns that may be missed by conventional monitoring approaches, potentially enabling more proactive maintenance interventions and enhanced flight safety.