Digital Twin-Enabled Autonomous Robotic Maintenance Frameworks

  • Authors

    • Ole Secher Olesen Professor, Aalborg University, Denmark. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PI6C4Y

    Published 04-03-2024

  • Digital Twin, Autonomous Robotics, Predictive Maintenance, Industry 5.0, Intelligent Automation, Artificial Intelligence, Industrial IoT, Edge Computing, Machine Learning, Robotic Health Monitoring

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    O. S. Olesen, “Digital Twin-Enabled Autonomous Robotic Maintenance Frameworks”, IJIARE, vol. 7, no. 1, pp. 01–08, Apr. 2024, doi: 10.67228/30715725/IJIARE-2024PI6C4Y.

  • Abstract

    The rapid evolution of Industry 5.0 has accelerated the integration of intelligent automation, artificial intelligence (AI), Industrial Internet of Things (IIoT), and cyber-physical systems into modern manufacturing environments. One of these newly developed technologies, DT technology has recently emerged as one of the transformational paradigms in realising predictive intelligence, autonomous maintenance and online operational optimisation of robotic systems. Conventional robotic maintenance strategies, such as corrective and preventive maintenance often lead to unexpected downtimes, unnecessary resources allocation and inflated maintenance costs largely due to the nature of scheduled inspections or post-failure interventions. The maintenance frameworks enabled by Digital Twin overcome these limitations as they deterministically establish a dynamically-updated virtual representation of physical robotic assets connecting sensor networks, cloud-edge computing, AI analytics and real-time simulation. This paper proposes a full Digital Twin-based autonomous robotic maintenance framework along with data acquisition from multiple sensors, edge intelligence, machine learning (ML)-based diagnosis and prediction of failures and autonomous decision-making for predictive maintenance. The proposed framework supports continuous monitoring of health, anomaly detection, RUL prediction and adaptive maintenance scheduling with minimal operational disruptions. A comparative analysis reveals that Digital Twin-assisted maintenance not only increases fault detection precision, maintenance efficiency, system availability, and operational reliability compared to traditional practices. It further discusses the current technological challenges, research gaps, and avenues for future work relating to federated Digital Twins, explainable AI (XAI), collaborative robotics, and sustainable intelligent maintenance systems. This framework lays a strong, scalable foundation for Industry 5.0 ecosystems of next generation autonomous robotic maintenance in the smart factory.

  • References

    [1] Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Springer.

    [2] Lee, J., Davari, H., Singh, J., & Pandhare, V. (2020). Industrial AI and predictive analytics for smart manufacturing systems. Manufacturing Letters, 18, 20–23.

    [3] Qi, Q., & Tao, F. (2022). Digital Twin and smart manufacturing. Engineering, 8(3), 245–256.

    [4] Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, challenges and enablers. IEEE Access, 8, 21980–22012.

    [5] Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2019). Digital Twin driven smart manufacturing. Journal of Manufacturing Systems, 54, 157–169.

    [6] Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.

    [7] Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for Industry 4.0. International Journal of Distributed Sensor Networks, 12(1), 1–12.

    [8] Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2021). A Digital Twin-driven cyber-physical system for intelligent manufacturing. Robotics and Computer-Integrated Manufacturing, 67, 102045.

    [9] Aivaliotis, P., Georgoulias, K., Arkouli, Z., & Makris, S. (2021). Degradation Curves Integration in Physics-Based Models: Towards the Predictive Maintenance of Industrial Robots. Robotics and Computer-Integrated Manufacturing, 71, 102177. https://doi.org/10.1016/j.rcim.2021.102177

    [10] Bondoc, A. E., Tayefeh, M., & Barari, A. (2022). Learning Phase in a LIVE Digital Twin for Predictive Maintenance. Autonomous Intelligent Systems, 2(1). https://doi.org/10.1007/s43684-022-00028-0

    [11] Kibira, D., & Weiss, B. A. (2022). Towards a Digital Twin of a Robot Workcell to Support Prognostics and Health Management. Proceedings of the 2022 Winter Simulation Conference (WSC). IEEE. https://doi.org/10.1109/WSC57314.2022.10015371

    [12] 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.

  • Downloads

Similar Articles

11-20 of 84

You may also start an advanced similarity search for this article.