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authormjkwiatkowski <mati.rewa@gmail.com>2026-05-17 14:21:09 +0200
committermjkwiatkowski <mati.rewa@gmail.com>2026-05-17 14:21:09 +0200
commita5a140c6286e8b113ca8d371f88e3ed54e731cea (patch)
treecd648c36df09d30c217166865a81a0c4e523932b /images
parenta4102d0252236e85b2813160b4b11e3a19a00d62 (diff)
feat: added lots of citations and slowly finishing the introduction
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-rw-r--r--images/S0007850618300799.bib14
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diff --git a/images/S0007850618300799.bib b/images/S0007850618300799.bib
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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
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