会议论文

Zhang, Xiaoyong

英文

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)

2153-0009

2018

1757-1762

21st IEEE International Conference on Intelligent Transportation Systems (ITSC)

IEEE International Conference on Intelligent Transportation Systems-ITSC

NOV 04-07, 2018

Maui, HI

[Wang, Shengnan;Zhang, Xiaoyong;Gao, Dianzhu;Chen, Bin;Cheng, Yijun;Yang, Yingze;Huang, Zhiwu;Peng, Jun] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China.^[Yu, Wentao] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Hunan, Peoples R China.^[Wang, Shengnan;Zhang, Xiaoyong;Gao, Dianzhu;Chen, Bin;Cheng, Yijun;Yang, Yingze;Yu, Wentao;Huang, Zhiwu;Peng, Jun] Hunan Engn Lab Rail Vehicles Braking Technol, Changsha, Hunan, Peoples R China.^[Gao, Dianzhu] CRRC ZhuZhou Locomot Co Ltd, Zhuzhou, Peoples R China.

IEEE, IEEE Intelligent Transportat Syst Soc

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-7281-0323-5

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61772558, 61672537, 61672539, 61502055, 61503048, 616025529]; Hunan Science and Technology Plan Project [2016GK2003]; Fundamental Research Funds for the Central Universities of Central South University [2018zzts540]

本校为其他机构

In transportation fields, safety, efficiency, and reliability of engines are primary concerns. Remaining useful life (RUL) estimation technology is used to assess the current health status and make effective maintenance plans for engines. How to estimate RUL accurately to increasing the reliability and safety of systems is a challenge issue. However, the existing estimation methods focus on single size sequence. To address it, this paper proposes a remaining useful life prediction model based on hybrid long-short sequences for engines. For long sequence, long shortterm memory neural network is...