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

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.