基于Autoencoder-TCN的航空发动机排气温度预测

针对目前航空发动机排气温度预测模型精度不高、传统RNN类神经网络对飞行数据时间维度信息挖掘不充分的问题，提出了一种结合自编码器Autoencoder和时间卷积神经网络TCN的航空发动机排气温度预测模型。首先通过Autoencoder方法从飞行数据中提取与排气温度相关的特征，以降维后的特征作为输入，建立TCN网络深度学习模型，以航空发动机排气温度作为输出，充分挖掘飞行数据的时间维度信息，从而提高模型精度。最后选取真实飞行数据进行实验，结果表明，与BP、LSTM神经网络模型相比，该模型的平均绝对百分比误差由13.035%和9.593%降低至3.369%，有效提高了模型预测精度。

Prediction of Aeroengine Exhaust Gas temperature based on Autoencoder-TCN Model

Aimed at the problems that the prediction of aero-engine exhaust gas temperature is low in precision, and the mining of the time dimension information of flight data by traditional RNN neural networks is inadequate, an exhaust gas temperature prediction model is proposed in combination with Autoencoder neural network and temporal convolutional network (TCN). First, the features related to the exhaust gas temperature (EGT) from the flight data are extracted by the Autoencoder method, and then the reduced dimensional features are taken as the input to build up a TCN network deep learning model. Secondly, the exhaust gas temperature of the aero engine is used as the output to fully mine the flight data dimensional information, improving model accuracy. Finally, the actual flight data is selected to be an experiment. The results show that compared BP to LSTM, the average absolute percentage error is reduced from 13.035%, 9.593% to 3.369% respectively, effectively improving the prediction accuracy of the model.