Automated Rotorcraft Turboshaft Engine Performance Prediction Using a Transfer Learning Approach

Accurately predicting turboshaft engine health is critical for ensuring the safe operation of helicopters supporting heavy-lift operations. Even though various approaches exist to predict engine health and available power in a rotorcraft, it is more desirable to develop effective and efficient methods that are generalized enough across different engine platforms and utilize existing aircraft data parameters for real-time or near real-time engine performance estimation. This paper presents a transfer learning-based approach for real-time performance prediction of rotorcraft turboshaft engines. The presented method utilizes rotorcraft parameter data recorded by a health and usage monitoring system (HUMS) or flight data monitoring system (FDMS) to accurately determine and predict engine performance deterioration over various operating conditions. U sing the data source domain adaptation capability of the transfer learning, the presented method provides the means to monitor and evolve the engine performance models over time. It can be broadly applicable to different engines. Real rotorcraft turboshaft engine data is used in a demonstration of the presented method.