Digital Twins in IT: Enhancing System Monitoring and Predictive Maintenance
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Digital twin technology is transforming IT infrastructure management by enabling real-time system monitoring and predictive maintenance. A digital twin is a virtual representation of a physical IT system that continuously updates based on real-time data, allowing for enhanced visibility, performance analysis, and failure prediction. With the integration of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) sensors, and cloud computing, digital twins are redefining how IT systems are managed, minimizing downtime and optimizing resource utilization.
This paper explores the architecture, applications, and benefits of digital twins in IT, focusing on their role in proactive maintenance and system optimization. It highlights how real-time monitoring through digital twins enables early detection of system anomalies, predictive analytics for failure prevention, and cost-effective maintenance strategies. The study presents comparative insights between traditional IT monitoring approaches and AI-powered digital twin solutions, demonstrating the superior efficiency and accuracy of the latter.
Furthermore, the paper discusses the challenges associated with digital twin implementation, including high initial costs, data security concerns, and integration complexities. Emerging trends such as self-learning AI models, blockchain-enhanced security, and the increasing adoption of digital twins in cloud computing are also examined. The research is supported by tables and graphs illustrating cost savings, efficiency improvements, and market growth projections in digital twin applications.
The findings suggest that digital twins will play a critical role in IT infrastructure management, offering enterprises a scalable and intelligent approach to optimizing their systems. Despite existing barriers, ongoing advancements in AI, data analytics, and cybersecurity are expected to drive wider adoption and greater operational benefits in the coming years.
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1. Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-820.
2. Pylianidis, C., Osinga, S., & Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, 105942.
3. Juarez, M. G., Botti, V. J., & Giret, A. S. (2021). Digital twins: Review and challenges. Journal of Computing and Information Science in Engineering, 21(3), 030802.
4. Herwig, C., Pörtner, R., & Möller, J. (Eds.). (2021). Digital twins. Berlin/Heidelberg, Germany: Springer.
5. Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383.
6. Boschert, S., & Rosen, R. (2016). Digital twin—the simulation aspect. Mechatronic futures: Challenges and solutions for mechatronic systems and their designers, 59-74.
7. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, 85-113.
8. Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia manufacturing, 11, 939-948.
9. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE access, 8, 108952-108971.
10. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on industrial informatics, 15(4), 2405-2415.
11. Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017). Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP annals, 66(1), 137-140.
12. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2010). Draft modeling, simulation, information technology & processing roadmap. Technology area, 11, 1-32.
13. Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818).
14. Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. Ifac-papersonline, 48(3), 567-572.
15. Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing, 9, 113-120.
16. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. Ifac-PapersOnline, 51(11), 1016-1022.
17. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Ieee Access, 6, 3585-3593.
18. Ríos, J., Hernández, J. C., Oliva, M., & Mas, F. (2015). Product avatar as digital counterpart of a physical individual product: Literature review and implications in an aircraft. Transdisciplinary Lifecycle Analysis of Systems, 657-666.
19. Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP annals, 66(1), 141-144.
20. Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X., & Zhang, D. (2019). Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of ambient intelligence and humanized computing, 10, 1155-1166.
Copyright (c) 2023 Gireesh Kambala
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