Digital twin driven prognostics and health management for complex equipment

被引：625

作者：

Tao, Fei ^{[1
]}

Zhang, Meng ^{[1
]}

Liu, Yushan ^{[1
]}

Nee, A. Y. C. ^{[2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[2] Natl Univ Singapore, Dept Mech Engn, Singapore 117576, Singapore

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2018年 / 67卷 / 01期

关键词：

Maintenance; Condition monitoring; Digital twin;

D O I：

10.1016/j.cirp.2018.04.055

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Prognostics and health management (PHM) is crucial in the lifecycle monitoring of a product, especially for complex equipment working in a harsh environment. In order to improve the accuracy and efficiency of PHM, digital twin (DT), an emerging technology to achieve physical-virtual convergence, is proposed for complex equipment. A general DT for complex equipment is first constructed, then a new method using DT driven PHM is proposed, making effective use of the interaction mechanism and fused data of DT. A case study of a wind turbine is used to illustrate the effectiveness of the proposed method. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.

引用

页码：169 / 172

页数：4

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