Paper
28 April 2023 Application of TCN algorithm in aircraft system
Bin Tan, Qiuni Li, Tingliang Zhang
Author Affiliations +
Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126262B (2023) https://doi.org/10.1117/12.2674397
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Tan, Qiuni Li, and Tingliang Zhang "Application of TCN algorithm in aircraft system", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126262B (28 April 2023); https://doi.org/10.1117/12.2674397
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KEYWORDS
Windows

Convolution

Data modeling

Mathematical optimization

Education and training

Correlation coefficients

Machine learning

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