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Digital twin driven prognostics and health management for complex equipment

  • Beihang University
  • National University of Singapore

科研成果: 期刊稿件文章同行评审

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.

源语言英语
页(从-至)169-172
页数4
期刊CIRP Annals
67
1
DOI
出版状态已出版 - 1 1月 2018

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