A Hybrid Digital Twin Scheme for the Condition Monitoring of Industrial Collaborative Robots

Samuel Ayankoso, Eric Kaigom, Hassna Louadah, Hamidreza Faham, Fengshou Gu, Andrew Ball

Research output: Contribution to journalConference articlepeer-review


Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots.

Original languageEnglish
Pages (from-to)1099-1108
Number of pages10
JournalProcedia Computer Science
Early online date20 Mar 2024
Publication statusPublished - 20 Mar 2024
Event5th International Conference on Industry 4.0 and Smart Manufacturing - Lisbon, Portugal
Duration: 22 Nov 202324 Nov 2023
Conference number: 5

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