Aero-engine exhaust gas temperature prediction based on LightGBM optimized by improved bat algorithm

In this paper, an aero-engine exhaust gas temperature prediction model based on LightGBM optimized by the chaotic rate bat algorithm is proposed to monitor aero-engine performance effectively. By introducing chaotic rate, the convergence speed and precision of bat algorithm are improved, which chaotic rate bat algorithm is obtained. The LightGBM is optimized by chaotic rate bat algorithm and it is used to predict exhaust gas temperature. Taking a type of aero-engine for example, some relevant performance parameters from the flight data measured by airborne sensors were selected as input variables and exhaust gas temperature as output variables. The data set is divided into training and test sets, and the CRBA-LightGBM model is trained and tested, and compared with ensemble algorithms such as RF, XGBoost, GBDT, LightGBM, and BA-LightGBM. The results show that the mean absolute error of this method in the prediction of exhaust gas temperature (after normalization) is 0.0065, the mean absolute percentage error is 0.77% and goodness of fit R2 has reached to 0.9469. The prediction effect of CRBA-LightGBM is better than other comparison algorithms and it is suitable for aero-engine condition monitoring.