LAUSR

In this paper, an aero-engine exhaust gas temperature (EGT) prediction model based on LightGBM optimized by the chaotic rate bat algorithm (CRBA) is proposed to monitor aero-engine performance effectively. By introducing chaotic rate, the convergence speed and precision of bat algorithm are improved, which CRBA is obtained. LightGBM is optimized by CRBA and it is used to predict EGT. 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 EGT 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 (MAE) of this method in the prediction of EGT (after normalization) is 0.0065, the mean absolute percentage error (MAPE) is 0.77% and goodness of fit 2 R 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.