Prediction of aero engine residual life based on chaotic genetic algorithm to optimize TCN network

Ke Tang; Ming Cai; Yao Li

doi:10.1117/12.3029059

22 May 2024 Prediction of aero engine residual life based on chaotic genetic algorithm to optimize TCN network

Ke Tang, Ming Cai, Yao Li

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760C (2024) https://doi.org/10.1117/12.3029059
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

The prediction result of Remaining Useful Life (RUL) of aero engine determines the timing of engine maintenance according to the condition, which is of great significance to the operation safety of the engine. In order to improve the prediction accuracy of aero-engine residual life, a chaotic genetic algorithm optimization sequential convolutional network (TGA-TCN) based residual life prediction method is proposed. The time dependence relationship of time series data is constructed based on time series convolutional network, and the optimal network structure is constructed by genetic algorithm and hyperparameter design. A turbofan engine degradation dataset (C-MPASS) is used to verify that the prediction accuracy of the model is significantly improved than that of CNN, LSTM, TCN, GA-TCN, etc.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ke Tang, Ming Cai, and Yao Li "Prediction of aero engine residual life based on chaotic genetic algorithm to optimize TCN network", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760C (22 May 2024); https://doi.org/10.1117/12.3029059

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