Digital Twin and Deep Reinforcement Learning Integrated Framework for Predictive Maintenance of Smart Damped Structures
Keywords:
digital twin; deep reinforcement learning; predictive maintenance; smart damped structures; structural health monitoring; cyber-physical systemsAbstract
Smart damped structures that embed viscoelastic layers and active elements are increasingly deployed in aerospace, civil, and automotive sectors to suppress vibration and extend service life. Their long-term resilience, however, depends on predictive maintenance strategies that can anticipate degradation of damping properties and structural integrity under stochastic operational loads. This paper presents a system-level integrated framework that couples high-fidelity digital twins with deep reinforcement learning agents to enable autonomous, condition-based maintenance planning for such complex mechatronic assemblies. The digital twin fuses physics-based modeling, in situ sensing, and continual model updating to mirror the evolving state of the structure, while the deep reinforcement learning module translates this state representation into maintenance policies that balance inspection cost, downtime, and residual life. We discuss the architecture and data-flow design of the twin–agent coupling, emphasizing modularity, real-time feedback, and edge-cloud orchestration. The analysis further examines structural trade-offs concerning governance, security, cross-organizational data sharing, and the robustness of decisions under epistemic and aleatory uncertainty. Policy dimensions, including fairness in asset fleet management and algorithmic accountability, are explored alongside implications for lifecycle sustainability and circular economy transitions. By integrating prognostics, control, and autonomous decision-making, the proposed framework offers a pathway toward self-aware damped structures that not only perceive their own health but also continuously optimize their maintenance in a resource-efficient and ethically grounded manner.
References
1. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
2. Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011). Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011, 154798. https://doi.org/10.1155/2011/154798
3. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
4. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
5. Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
6. Lu, J., Zhan, Z., Liu, X., & Wang, P. (2018). Numerical modeling and model updating for smart laminated structures with viscoelastic damping. Smart Materials and Structures, 27(7), 075038.
7. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
8. Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012
9. 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. https://doi.org/10.1016/j.ifacol.2015.06.141
10. 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. https://doi.org/10.1109/ACCESS.2018.2793265
11. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. In Proceedings of the International Conference on Learning Representations. https://arxiv.org/abs/1509.02971
12. Boller, C., & Meyendorf, N. (2018). State-of-the-art in structural health monitoring for aeronautics. In C. Boller (Ed.), Structural health monitoring in aeronautics (pp. 1–42). Springer. https://doi.org/10.1007/978-3-319-79883-8_1
13. Ciang, C. C., Lee, J. R., & Bang, H. J. (2008). Structural health monitoring for a wind turbine system: a review of damage detection methods. Measurement Science and Technology, 19(12), 122001. https://doi.org/10.1088/0957-0233/19/12/122001
14. Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836. https://doi.org/10.1016/j.ymssp.2010.11.018
15. Worden, K., & Dulieu-Barton, J. M. (2004). An overview of intelligent fault detection in systems and structures. Structural Health Monitoring, 3(1), 85–98. https://doi.org/10.1177/1475921704041866
16. De Silva, C. W. (2006). Vibration damping, control, and design. CRC Press.
17. Andriotis, C. P., & Papakonstantinou, K. G. (2019). Managing engineering systems with large state and action spaces through deep reinforcement learning. Reliability Engineering & System Safety, 191, 106483. https://doi.org/10.1016/j.ress.2019.106483
18. Mottershead, J. E., & Friswell, M. I. (1993). Model updating in structural dynamics: a survey. Journal of Sound and Vibration, 167(2), 347–375. https://doi.org/10.1006/jsvi.1993.1340
19. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
20. Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757–768. https://doi.org/10.1016/j.jclepro.2016.12.048
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
