The concept of twin digital models as support for production and service processes of technical facilities

• Autorzy: Simiński Przemysław

Identyfikatory

DOI

10.34739/si.2023.28.06

• Języki publikacji: EN

Abstrakty

EN

The article presents the concept of using twin digital models as support for production and service processes of technical facilities. The implementation of the concept can be helpful in diagnostic processes and proactively diagnose the condition of technical objects in real time and report actions to ensure the continuity of processes. The concept assumes the creation of a digital model of a technical object for the purposes of improving the operation processes of the machine park (means of transport, means of production). A comprehensive and integrated solution will improve work efficiency and lower service costs.

Słowa kluczowe

EN

mathematical model; diagnostics; prediction; exploitation

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2023
• Tom: Vol. 1(28)
• Strony: 109--121
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Simiński Przemysław

• Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences,Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

• https://orcid.org/0000-0002-2323-3152+

Bibliografia

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• 2. Batty, Michael. "Digital twins. Environment and Planning B: Urban Analytics and City Science 45.5 (2018): 817-820.
• 3. Chen, L., Zhang, X., & Li, Y. Digital twin for building energy efficiency: A review. Renewable and Sustainable Energy Reviews (2022), 168, 109797.
• 4. Jiang, Yuchen, et al. Industrial applications of digital twins. Philosophical Transactions of the Royal Society A 379.2207 (2021): 20200360.
• 5. Li, Y., Li, Z., & Li, S. A survey on digital twins for industrial applications. IEEE Transactions on Industrial Informatics, 18(10) (2022), 12131-12147.
• 6. Nam, David, et al. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Reports (2022): 100443.
• 7. Nizinski, S., and Arkadiusz Rychlik. Model diagnostyczny złożonego obiektu technicznego. Biuletyn Wojskowej Akademii Technicznej 60.1 (2011): 195-209.
• 8. Wang, Jinjiang, et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing." International Journal of Production Research 57.12 (2019): 3920-3934.
• 9. Wang, Zongyan. Digital twin technology. Industry 4.0-Impact on Intelligent Logistics and Manufacturing. IntechOpen, 2020.
• 10. Wesołowski Z., Identification of systems reliability. Studia Informatica Vol. 15 (2011).
• 11. Wu, Y., Zhang, Y., Li, X., & Wang, Y. (2022). Digital twin-driven manufacturing optimization: A review. Journal of Manufacturing Systems, 54, 101-112.
• 12. Tchórzewski J., Metody sztucznej inteligencji i informatyki kwantowej w ujęciu teorii sterowania i systemów, Wydawnictwo Naukowe Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach, Siedlce 2021.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)