Prediction Method of APU Performance Parameters based on Neural Network

Accurate prediction of the Exhaust Gas Temperature (EGT) of the Aircraft Auxiliary Power Unit (APU) can effectively monitor the future operating status of the APU and prevent safety accidents from occurring. An APU performance parameter prediction method that incorporates Atrous Convolutional Neural Network (ACNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Attention mechanism is proposed. By combining the characteristics of different networks, the purpose of improving the accuracy of EGT prediction is achieved. ACNN is introduced to extract features from the input parameters, and then the Bi-LSTM network is used to learn and train the extracted feature parameters and combine with the Attention mechanism to give different weights to different feature parameters to achieve parameter prediction. The experimental results show that for single-step and multi-step prediction of EGT, the prediction accuracy of the proposed model is significantly improved compared with other prediction models, providing a certain reference for APU performance change trend prediction.