Robust Health Condition Prediction of Helicopter Turboshaft Engines Using Ensemble Machine Learning Models

Abstract

This paper presents a novel ensemble approach that combines multiple machine-learning algorithms to deliver robust predictions of helicopter turboshaft engine health status (nominal or faulty) using operational data. Engine health is evaluated through the torque margin, defined as the percentage difference between the measured and target torque values. A Gaussian process model is used to estimate the torque margin as a probability distribution function (PDF), and these predictions are incorporated as features into various machine-learning models. These models are then employed to perform binary classification, determining the engine's health state. To enhance performance, a reference set is defined for each unseen data point, allowing a comparison of the relative performances of the models, with the best performer selected for the final prediction. Our ensemble method achieves high accuracy in health classification while providing precise torque margin estimates. The results demonstrate that ensemble models offer superior generalization and reliability compared to individual machine-learning algorithms, especially when applied to complex, multivariate datasets like those from helicopter turboshaft engines.