Received 30.01.2026, Revised 18.05.2026, Accepted 25.06.2026 Published 03.08.2026

Practical IT/OT integration of an edge-cloud hybrid digital twin for ship power plants

Vladimir Vychuzhanin*, Alexey Vychuzhanin

v.v.vychuzhanin@op.edu.ua



The increasing complexity and stringent real-time reliability requirements of modern ship power plants (SPPs) render conventional digital twins insufficient when relying solely on either physics-based or data-driven models. Furthermore, the lack of unified IT/OT integration and semantic consistency limits their practical deployment in operational maritime environments. The aim of this study was to develop and validate a hybrid digital twin (HDT) framework that ensures adaptive modelling, semantic interoperability, and secure real-time integration within the ship’s cyber-physical infrastructure. To achieve this, a cognitively adaptive HDT architecture was proposed. It merged physicsbased thermodynamic models with data-driven CNN-LSTM components via a dynamic authority-shift mechanism governed by real-time uncertainty. The framework incorporated a cognitive simulation model (CSM) based on OWL 2.0 ontologies and SWRL rules for the semantic validation of physical constraints directly within the operational loop. Additionally, a semantic-functional dual exchange (SFDX) protocol was introduced to unify physical telemetry and ontological descriptors. This integration of cognitive validation and dynamic model balancing ensured robust operation under sensor degradation and cyber-physical disturbances. Experimental validation was performed using a Hardware-inthe-Loop testbed simulating real SPP conditions. The results demonstrated stable operation exceeding 10⁴ messages per second, with end-to-end latency below 0.25 seconds and data loss under 0.3%. The hybrid model reduced mean remaining useful life prediction error by 20-25% compared to standalone approaches, maintaining physical-semantic consistency below a 3% deviation threshold. Architectural evaluation confirmed the effectiveness of a distributed onboard-edge-cloud paradigm, executing real-time control locally while delegating computationally intensive retraining to higher tiers. The proposed framework established a scalable foundation for trustworthy and cognitively integrated SPP digital twins (DTs)

cyber-physical systems; semantic interoperability; adaptive modelling; remaining useful life; cognitive simulation
145-161
Vychuzhanin, V., & Vychuzhanin, A. (2026). Practical IT/OT integration of an edge-cloud hybrid digital twin for ship power plants. Information Technologies and Computer Engineering, 23(2), 145-161. https://doi.org/10.31649/vitce/2.2026.145

References

  1. Al-Mhiqani, M.N., Alsboui, T., Al-Shehari, T., Abdulkareem, K.H., Ahmad, R., & Mohammed, M.A. (2024). Insider threat detection in cyber-physical systems: A systematic literature review. Computers and Electrical Engineering, 119, article number 109489. doi: 10.1016/j.compeleceng.2024.109489.
  2. Boschert, S., & Rosen, R. (2016). Digital twin – the simulation aspect. In P. Hehenberger & D. Bradley (Eds.) Mechatronic futures (pp. 59-74). Cham: Springer. doi: 10.1007/978-3-319-32156-1_5.
  3. Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, article number 103130. doi: 10.1016/j.compind.2019.103130.
  4. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971. doi: 10.1109/ACCESS.2020.2998358.
  5. Grangel-González, I., Halilaj, L., Coskun, G., Auer, S., Collarana, D., & Hoffmeister, M. (2016). Towards a semantic administrative shell for Industry 4.0. In Proceedings of IEEE ICSC. Laguna Hills: IEEE Computer Society Conference Publishing Services. doi: 10.1109/ICSC.2016.58.
  6. Heluany, J.B., & Gkioulos, V. (2024). A review on digital twins for power generation and distribution. International Journal of Information Security, 23, 1171-1195. doi: 10.1007/s10207-023-00784-x.
  7. Homaei, M., Mogollón-Gutiérrez, Ó., Sancho, J.C., Ávila, M., & Caro, A. (2024). A review of digital twins and their application in cybersecurity based on artificial intelligence. Artificial Intelligence Review, 57, article number 201. doi: 10.1007/s10462-024-10805-3.
  8. Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3, 422-440. doi: 10.1038/s42254-021-00314-5.
  9. Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., & Xu, X. (2020). Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, article number 101837. doi: 10.1016/j.rcim.2019.101837.
  10. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and Industry 4.0. Engineering, 6, 3585-3593. doi: 10.1109/ACCESS.2018.2793265.
  11. Raghavendra Babu, T.M., & Harish Kumar, K.S. (2025). Intelligent security model for digital twins: An autoencoder-based anomaly detection framework. Journal of Information Systems Engineering and Management, 10(56).
  12. Suhail, S., Hussain, R., Jurdak, R., & Hong, C.S. (2021). Trustworthy digital twins in the industrial Internet of Things with blockchain. IEEE Internet Computing, 26(3), 58-67. doi: 10.1109/MIC.2021.3059320.
  13. Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372-389. doi: 10.1016/j.jmsy.2022.06.015.
  14. Tyagi, A.K., Tiwari, S., Arumugam, S.K., & Sharma, A.K. (2024). Artificial intelligence-enabled digital twin for smart manufacturing. Beverly: Scrivener Publishing LLC. doi: 10.1002/9781394303601.
  15. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C., & Hu, Z. (2022). A comprehensive review of digital twin: Modeling and enabling technologies. Structural and Multidisciplinary Optimization, 65, article number 354. doi: 10.1007/s00158-022-03425-4.
  16. Vychuzhanin, V., & Vychuzhanin, O. (2026). A hybrid model for evaluating the accuracy of failure forecasts in ship power plants. BULLETIN of Cherkasy State Technological University, 31(1), 44-59. doi: 10.62660/bcstu/1.2026.44.
  17. Vychuzhanin, V., & Vychuzhanin, O. (2025). Digital methods and models for control and survivability of complex technical systems. Lviv: Liha-Pres. doi: 10.36059/978-966-397-556-6.
  18. Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T.H., & Liu, Y. (2023). A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet of Things Journal, 10(17), 14965-14987. doi: 10.1109/JIOT.2023.3263909.
  19. Xia, Y., Xiao, Z., Jazdi, N., & Weyrich, M. (2024). Generation of asset administration shell with large language model agents: Toward semantic interoperability in digital twins in the context of industry 4.0. IEEE Access, 12, 84863-84877. doi: 10.1109/ACCESS.2024.3415470.
  20. Zhao, Y., Hu, Z., Du, W., Geng, L., & Yang, Y. (2024). Research on modeling method of autonomous underwater vehicle based on a physics-informed neural network. Journal of Marine Science and Engineering, 12(5), article number 801. doi: 110.3390/jmse12050801.