TY  - EJOU
AU  - Wu, Jun  
AU  - Hu, Kui  
AU  - Cheng, Yiwei  
AU  - Wang, Ji  
AU  - Deng, Chao  
AU  - Wang, Yuanhan  

TI  - Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines
T2  - Structural Durability \& Health Monitoring

PY  - 2019
VL  - 13
IS  - 3
SN  - 1930-2991

AB  - Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN), including ordinary RNN, long short-term memory (LSTM), and gated recurrent unit (GRU), are individually constructed. Thirdly, ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction. The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets. Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures.
KW  - Aircraft engines
KW  -  residual useful life prediction
KW  -  health monitoring
KW  -  neural networks
KW  -  ensemble learning

DO  - 10.32604/sdhm.2019.05571