Aiming at the drawback that performance parameter prediction method of auxiliary power unit(APU) based on traditional machine learning cannot make full use of the time series and non-linear characteristics between parameter data, a prediction method of APU performance parameters based on convolutional neural network (CNN)-long short-term memory(LSTM)-attention was proposed. First, a one-dimensional CNN was introduced, and abstract features of different attributes were obtained through the preprocessed parameter data. Then, LSTM neural network was used to memorize these features and combined with an Attention mechanism that could assign different weights to the feature states to achieve parameter prediction. The parameter data of a certain type of APU was used to predict exhaust gas temperature(EGT) at different steps in the future. The experimental results show that for the prediction of single-step EGT, CNN-LSTM-Attention model reduces mean absolute percentage error(MAPE) index respectively by 15.2%, 32.5%, and 60.3%, compared with the CNN-LSTM, LSTM, and simple recurrent neural network(Simple RNN) models, and reduces root mean square error(RMSE) index by 7.3%, 11.6%, and 32.9%. At the same time, it has higher prediction accuracy in multi-step EGT prediction, which proves the effectiveness of this method and provides a certain reference for short-term APU performance change trend prediction. © 2022, Editorial Department of Journal of Propulsion Technology. All right reserved.
