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
im-proved, 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 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.