Digital Twin Lifecycle: Core Challenges and Open Issues
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-24291-5_13
Reference45 articles.
1. Hu, S., Wang, S., Su, N., Li, X., Zhang, Q.: Digital twin based reference architecture for petrochemical monitoring and fault diagnosis. Oil Gas Sci. Technol. Rev. IFP Energies Nouv. 76, 9 (2021). https://doi.org/10.2516/ogst/2020095
2. Castellani, A., Schmitt, S., Squartini, S.: Real-world anomaly detection by using digital twin systems and weakly-supervised learning. IEEE Trans. Ind. Inf. 17(7), 4733–4742 (2021). https://doi.org/10.1109/TII.2020.3019788
3. Classens, K., Heemels, W.P.M.H.M., Oomen, T.: Digital twins in mechatronics: from model-based control to predictive maintenance. In: 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), Beijing, China, pp. 336–339 (2021). https://doi.org/10.1109/DTPI52967.2021.9540144
4. Macchi, M., Roda, I., Negri, E., Fumagalli, L.: Exploring the role of digital twin for asset lifecycle management. IFAC-PapersOnLine 51(11), 790–795 (2018). https://doi.org/10.1016/j.ifacol.2018.08.415
5. Fathy, Y., Jaber, M., Nadeem, Z.: Digital twin-driven decision making and planning for energy consumption. JSAN 10(2), 37 (2021). https://doi.org/10.3390/jsan10020037
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