Ensemble Integration Methods for Load Estimation

Abstract

Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. To increase confidence in the load estimation process, ensemble methods are employed combining multiple individual load estimators that increase predictive stability across flights and add robustness to noisy data. In this work, several load estimation methods are applied to a variety of machine learning algorithms to build a large library of individual load estimation models for main rotor yoke loads from 28 flight state and control system parameters. This paper explores several ensemble integration methods including simple averaging, weighted averaging using rank sum, and forward selection. From the 426 individual models, 25 top models were selected based on four ranking metrics, root mean squared error (RMSE), correlation coefficient, and interquartile ranges of these two metrics. All ensembles achieved improved performance for these four metrics compared to the best individual model, with the forward selection ensemble obtaining the lowest RMSE, highest correlation, and closest load signal prediction visually of all models.