Published at: Electronics

Data-Driven Estimation of Helicopter Engine Power Using Regular Flight Data: A Machine Learning Approach

Abstract:
The accurate estimation of helicopter engine power is crucial for ensuring operational performance and maintaining safety. Current methods, such as Maximum Power Checks (MPCs), are effective but resource-intensive and infrequent. This paper presents a novel machine learning-based framework tailored for operational helicopter fleets to estimate Engine Torque Factor (ETF) values from routine flight data obtained via Health and Usage Monitoring Systems (HUMS). The novelty lies in combining a statistically validated labeling strategy that links MPC-derived ETF values to regular flights with a dual-stage preprocessing pipeline, consisting of steady-state filtering and data consolidation, which is designed to produce high-quality, representative training data from noisy operational logs. Regression models, including XGBoost, CatBoost, and Random Forest, were trained and evaluated using HUMS data from AH-64A helicopters. Results demonstrate that focusing on specific ETF ranges significantly improves model performance, achieving R2 values of up to 0.94. While the current implementation operates post-flight, the approach enables continuous monitoring between scheduled MPCs, potentially reducing unnecessary checks and providing earlier indications of power degradation.

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