Open AccessRemaining Useful Life Prediction of Turbofan Engine Based on Temporal Convolutional Networks Optimized by Genetic Algorithm
Author(s) -
Zhengkun Chen,
Baojia Chen,
Xueliang Chen
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2181/1/012001
Subject(s) - turbofan , hyperparameter , mean squared error , residual , computer science , genetic algorithm , convolutional neural network , algorithm , mean squared prediction error , block (permutation group theory) , artificial neural network , sampling (signal processing) , pattern recognition (psychology) , artificial intelligence , machine learning , mathematics , statistics , engineering , geometry , filter (signal processing) , automotive engineering , computer vision
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.