Remaining useful life prediction for aircraft engine based on LSTM-DBN

被引：8
作者：
Li J. [1 ]
Chen Y. [1 ]
Xiang H. [1 ]
Cai Z. [1 ]
机构：
[1] Equipment Management & UAV Engineering College, Air Force Engineering University, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 07期
关键词：
Deep belief network (DBN); Health indicator; Long short-term memory (LSTM) network; Remaining useful life (RUL) prediction;
D O I：
10.3969/j.issn.1001-506X.2020.07.28
中图分类号：
学科分类号：
摘要：
In order to solve the problems of high dimension and large scale of multi-sensors monitoring data and insufficient consideration of time series information in the remaining useful life (RUL) prediction of the aircraft engine, a RUL prediction method based on the long short-term memory (LSTM) network and deep belief network (DBN) is proposed. Firstly, the time series of a single sensor is predicted by using the LSTM network. Secondly, the prediction results are integrated into the DBN to extract the health indicator. Thirdly, the RUL prediction results are obtained by combining the health indicator prediction curve and the failure threshold. Finally, to validate the feasibility and the effectiveness of the proposed method, an experiment is carried out on the commercial modular aero-propulsion system simulation data set and the prediction results are compared with the existing methods. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：1637 / 1644
页数：7
相关论文
共 23 条
[21]  
BABU G S, ZHAO P L, LI X L., Deep convolutional neural network based regression approach for estimation of remaining useful life, Proc.of the International Conference on Database Systems for Advanced Applications, pp. 214-228, (2016)
[22]  
SAXENA A, KAI G, SIMON D, Et al., Damage propagation modeling for aircraft engine run-to-failure simulation, Proc.of the International Conference on Prognostics and Health Management, (2008)
[23]  
SONG Y, XIA T B, ZHENG Y, Et al., Remaining useful life of turbofan engine based on Autoencoder-BLSTM, Computer Integrated Manufacturing Systems, 25, 7, pp. 1611-1619, (2019)
123