Prediction method of turbine engine RUL based on GA-SVR

Abstract

The remaining life prediction of turbine engine plays an indispensable role in engine health management, which is of great significance to ensure flight safety and improve maintenance efficiency. With the development of engine health management technology, the engine is terminated before failure or failure, which makes it difficult to collect enough data with failure information. In order to improve the prediction accuracy of engine remaining life with limited data samples, a joint algorithm based on genetic algorithm and support vector regression (GA-SVR) is proposed in this paper. Genetic algorithm (GA) is used to solve the hyperparametric optimization problem in support vector regression (SVR) model. Based on the C-MAPSS public data set provided by NASA, the data of 20 engines are randomly selected to construct a small sample data set to train the GA-SVR model, and compared with other existing algorithms. The experimental results show that the prediction error of GA-SVR model is smaller in the case of small samples, It is proved that the proposed model can accurately deal with the problem of turbine engine residual life prediction in the case of small samples.