Remaining Useful Life Prediction for Aero-Engine Based on Hybrid CNN-GRU Model

The prediction of remaining useful life (RUL) for an aero-engine is crucial to ensure the operation safety and reliability of an aircraft. Inspired by the data-driven method, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Neural Network (GRU) model to predict the RUL based on the multi-source heterogeneous sensor data in this study. The proposed hybrid CNN-GRU model takes the advantage that CNN can effectively extract the features of multi-sensor data on spatial-temporal dimensions, and GRU can figure out the problem of long-term dependence with the superiority of less complicated model structure in the processing of time-series data. Experiments on the NASA C-MAPSS dataset are conducted by using the proposed model, and the RUL prediction results are presented. The results show that the hybrid CNN-GRU model has an improvement in prediction accuracy compared with other single-network models.