Using Process Mining as a Tool for Process’ Digital Twin to Perform Strategic Maintenance Decisions
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-38165-2_6
Reference22 articles.
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2. Bousdekis A., Lepenioti K., Apostolou D., Mentzas G.: A review of data-driven decision-making methods for Industry 4.0 maintenance applications. Electronics 10(7) (2021). https://doi.org/10.3390/electronics10070828
3. Tao, F., Zhang, H., Liu, A., Nee, A.Y.C.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15(4), 2405–2415 (2019). https://doi.org/10.1109/TII.2018.2873186
4. Errandonea, I., Beltrán, S., Arrizabalaga, S.: Digital twin for maintenance: a literature review. Comput. Indust. 123 (2020). https://doi.org/10.1016/j.compind.2020.103316
5. Friederich, J., Francis, D.P., Lazarova-Molnar, S., Mohamed, N.: A framework for data-driven digital twins of smart manufacturing systems. Comput. Indust. 136 (2022). https://doi.org/10.1016/j.compind.2021.103586
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