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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.}
+} \ No newline at end of file