JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method

Abstract

In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, and the coefficient of determination (R2) values of the training, validation, and testing subsets are 0.99, 0.99, and 0.98, respectively. Based on a model sensitivity analysis, the four parameters’ optimal values are as follows: the number of estimators is 900; the learning rate is 0.1; the maximum depth is 4, and the random state is 3. In addition, a comparison between the model performance in this study and that in a previous one was conducted. The MSE value is as low as 0.000021.