Remaining Useful Life Prediction of Turbofan Engine Based on Temporal Convolutional Networks Optimized by Genetic Algorithm

Abstract

Abstract It is tough to select the parameters of the deep neural network and enhance the accuracy in the field of remaining useful life (RUL) prediction. To address the problem, one RUL prediction model optimized by a genetic algorithm, based on temporal convolutional networks (TCN), was proposed. Firstly, forward-filling sliding sampling is used to add the samples’ time step. Then the genetic algorithm is used to search the hyperparameters of the residual block of TCN. Finally, the performance of the proposed method is verified by the C-MAPSS dataset. The results show that in the two evaluation metrics of root mean square error (RMSE) and score function (SF), the proposed GA-TCN reduces them by 8.2% ∼ 27.56% and 28.24% ∼ 79.35%, respectively, when compared with other studies. The RMSE and SF metrics of the proposed method are on average 17.10% and 54.10% lower than that of other methods in four sub-datasets of the turbofan engine.