Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines  

作　　者：Jun Wu Kui Hu Yiwei Cheng Ji Wang Chao Deng Yuanhan Wang 

机构地区：[1]School of Naval Architecture and Ocean Engineering,Huazhong University of Science and Technology,Wuhan,430074,China [2]School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan,430074,China [3]China Electronic Product Reliability and Environmental Testing Research Institute,Guangzhou,510610,China

出　　处：《Structural Durability & Health Monitoring》2019年第3期317-329,共13页结构耐久性与健康监测（英文）

基　　金：the National Natural Science Foundationof China(Nos.11672098,11502063);the Natural Science Foundation of Anhui Province(No.1608085QA07).

摘　　要：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 shortterm 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.

关 键 词：Aircraft engines residual useful life prediction health monitoring neural networks ensemble learning 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]