Application of TCN algorithm in aircraft system

Abstract

Aiming at the problem of many aeroengine monitoring parameters, large amount of data, and timeliness of data, a novel aero-engine Remaining Useful Life (RUL) prediction method based on Temporal convolutional network (TCN) was proposed. Firstly, the data were redivided by setting different sliding window lengths, and then the optimal parameter selection of the model was studied. Finally, the remaining useful life prediction results of this method and traditional methods were compared and analyzed. The results showed that: The different parameters affected the conclusion of the calculation of the model. When the sliding window length was 30, the batch_size was 64, the dropout was 0.1, and the kenel_size was 8, the model had good prediction results. The best deterministic correlation coefficient between the predicted value and the actual value was 0.86, and the predicted trend of change was basically consistent with the actual value. The root mean square error of the model was 19.85, which was parallel to Long short-term memory (LSTM) and Convolutional neural networks (CNN), and the result verified the effectiveness of the method in predicting the remaining useful life of the engine. Through the above research, it provided a new model reference for solving the problem of engine remaining useful life prediction.