Prediction of remaining life of turbine engine based on data drive

Turbine engine systems have complex operating conditions and often work in extreme environments. They are prone to fail and cause irreparable losses. The prediction method based on physical model relies too much on prior knowledge, which makes it difficult to establish the model and has poor applicability. In order to establish an engine remaining life prediction model suitable for high-dimensional features and predict the engine remaining life more reasonably, the improved gradient lifting decision tree (GBDT) and normalized turbine engine performance data were used for experiments. The results show that the improved GBDT model is suitable for predicting the residual service life of aircraft engines under different operating conditions. The prediction effect is better than existing support vector regression(SVR), convolution neural network(CNN), Long Short-Term memory neural network(LSTM), etc. In particular, the running time has been greatly improved. It can provide guarantee for health management and operation and maintenance decisions of turbine engine. © 2021, Harbin University of Science and Technology Publication. All right reserved.