Health indicator construction and remaining useful life prediction for aircraft engine

被引：0
|
作者：
Peng K.-X. [1 ,2 ]
Pi Y.-T. [1 ,2 ]
Jiao R.-H. [1 ,2 ]
Tang P. [1 ,2 ]
机构：
[1] Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
[2] Institute of Artificial Intelligence, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Deep belief network; Health indicator; Health status recognition; Hidden Markov model; Remaining useful life prediction;
D O I：
10.7641/CTA.2019.90039
中图分类号：
学科分类号：
摘要：
Prognostics and health management can effectively evaluate the health status and predict the remaining useful life of the system. It is an important guarantee to improve the safety and economy of complex systems. In order to fully assess the health status of the system, an unsupervised health indicator construction method based on the deep belief network (DBN) is proposed in this paper, and remaining useful life of the system is predicted with the hidden Markov model (HMM). Firstly, the feature extraction of historical data is realized by unsupervised training deep belief network, and then the health indicator is constructed. Secondly, the health indicator set is used to train the hidden Markov model, then the automatic recognition of equipment health state can be realized. Finally, the remaining useful life of the system is calculated by the DBN-HMM hybrid model. To validate the effectiveness of the proposed approach, a case study is performed on the commercial modular aero-propulsion system simulation (C-MAPSS) aircraft engine datasets. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：713 / 720
页数：7
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