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@article{TAO2018169,
title = {Digital twin driven prognostics and health management for complex equipment},
journal = {CIRP Annals},
volume = {67},
number = {1},
pages = {169-172},
year = {2018},
issn = {0007-8506},
doi = {https://doi.org/10.1016/j.cirp.2018.04.055},
url = {https://www.sciencedirect.com/science/article/pii/S0007850618300799},
author = {Fei Tao and Meng Zhang and Yushan Liu and A.Y.C. Nee},
keywords = {Maintenance, Condition monitoring, Digital twin},
abstract = {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.}
}
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