Digital Twin implementation for condition monitoring of a collaborative robot
1
Faculty of Mechanical Engineering, Casimir Pulaski Radom University, Poland
Submission date: 2026-05-21
Final revision date: 2026-07-09
Acceptance date: 2026-08-10
Online publication date: 2026-08-17
Publication date: 2026-08-17
Corresponding author
KEYWORDS
TOPICS
ABSTRACT
The article presents the implementation and experimental verification of a Digital Twin framework for sensorless condition monitoring, understood as monitoring without the need for additional, non-native sensing hardware, of a Universal Robots UR3e collaborative robot. The system combines real-time data acquisition via the RTDE interface with a virtual reference model estimating nominal motor-current behaviour during repeatable motion cycles. The anomaly detection criterion is based on residuals between measured and predicted current signals, enabling the distinction between process disturbances and mechanical degradation. Validation on a CP-Factory workstation using a figure-eight trajectory, induced resistance and simulated payload loss shows sensitivity to low-amplitude anomalies and allows separation of friction increase, joint resistance and payload loss.
FUNDING
The presented research was funded with grant No.3584/183/P (DBUPB/2023/011) for education allocated by the Ministry of Science and Higher Education of the Republic of Poland.
REFERENCES (36)
1.
Cheng L, Gao H, Sun W, Chen C, Xu X. An integrated method for predictive state assessment and path planning for inspection robots in island-based unmanned substations. Eksploatacja i Niezawodność – Maintenance and Reliability. 2025;27(4):16. https://doi.org/10.17531/ein/2....
2.
International Organization for Standardization. ISO 10218-1:2025 Robotics - Safety requirements - Part 1: Industrial robots. Geneva: ISO; 2025.
3.
International Organization for Standardization. ISO/TS 15066:2016 Robots and robotic devices - Collaborative robots. Geneva: ISO; 2016.
4.
Rumin P, Kotowicz J, Zastawna-Rumin A. Predictive maintenance of belt conveyor idlers based on measurements, analytical calculations and decision-making algorithms. Eksploatacja i Niezawodność – Maintenance and Reliability. 2025;27(4):6. https://doi.org/10.17531/ein/2....
5.
Grieves M, Vickers J. Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen FJ, Flumerfelt S, Alves A, editors. Transdisciplinary perspectives on complex systems. Cham: Springer; 2016. https://doi.org/10.1007/978-3-....
6.
Mazumder A, Sahed M, Tasneem Z, Das P, Badal F, Ali M, et al. Towards next generation digital twin in robotics: Trends, scopes, challenges, and future. Heliyon. 2023;9(2):e13359. https://doi.org/10.1016/j.heli....
7.
Geronel RS, Silva MM. Digital twins in robotic applications. In: Avancos recentes em Ciencias da Engenharia: volume 1. Sao Carlos: Universidade de Sao Paulo; 2025. Available from: https://www.researchgate.net/p....
8.
International Organization for Standardization. ISO 23247-2:2021 Automation systems and integration - Digital twin framework for manufacturing - Part 2: Reference architecture. Geneva: ISO; 2021.
9.
International Organization for Standardization. ISO/IEC 30141:2024 Internet of Things (IoT) - Reference architecture. Geneva: ISO; 2024.
10.
Mo F, Rehman HU, Chaplin JC, Sanderson D, Ratchev S. Digital twin-based self-learning decision-making framework for industrial robots in manufacturing. Int J Adv Manuf Technol. 2025;139:221-240. https://doi.org/10.1007/s00170....
11.
Zhang Y, Gao P, Wang Z, He Q. Research on status monitoring and positioning compensation system for digital twin of parallel robots. Sci Rep. 2025;15:7432. https://doi.org/10.1038/s41598....
12.
Ayankoso S, Kaigom E, Louadah H, Faham H, Gu F, Ball A. A hybrid digital twin scheme for the condition monitoring of industrial collaborative robots. Procedia Comput Sci. 2024;232:1099-1108. https://doi.org/10.1016/j.proc....
13.
Ouarhlent S, Terki N, Hamiane M, Dahmani H. Fault detection in robots based on discrete wavelet transformation and eigenvalue of energy. Diagnostyka. 2023;24(4):2023407. https://doi.org/10.29354/diag/....
14.
Sabry AH, Ungku Amirulddin UAB. A review on fault detection and diagnosis of industrial robots and multi-axis machines. Results Eng. 2024;23:102397. https://doi.org/10.1016/j.rine....
15.
Zhang Y, Wu J, Gao B, Xia L, Lu C, Wang H, et al. Fault Types and Diagnostic Methods of Manipulator Robots: A Review. Sensors. 2025;25(6):1716. https://doi.org/10.3390/s25061....
16.
Khan Z, Nasir A, Mekid S. Fault-tolerant control strategies for industrial robots: state of the art and future perspective on AI-based fault management. Artif Intell Rev. 2025;58(11):362. https://doi.org/10.1007/s10462....
17.
Zhang G, Tao Y, Wang J, Feng K, Han X. A Motor current signal-based fault diagnosis method for harmonic drive of industrial robot under time-varying speed conditions. IEEE Trans Instrum Meas 2025;74:1-10. https://doi.org/10.1109/TIM.20....
18.
Hsieh N, Yu T. Fault detection in harmonic drive using multi-sensor data fusion and gravitational search algorithm. Machines. 2024;12(12):831. https://doi.org/10.3390/machin....
19.
Qiao Y, Wang H, Cao J, Lei Y. Sound-vibration spectrogram fusion method for diagnosis of RV reducers in industrial robots. Mech Syst Signal Process. 2024;214:111411. https://doi.org/10.1016/j.ymss....
20.
Elahi MU, Raouf I, Khalid S, Ahmad F, Kim HS. Transfer Learning-Based Health Monitoring of Robotic Rotate Vector Reducer Under Variable Working Conditions. Machines. 2025;13(1):60. https://doi.org/10.3390/machin....
21.
Ren G, Wang Z, Liu X, Song F. Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description. J Mech Sci Technol. 2024;38(8):4037-4051. https://doi.org/10.1007/s12206....
22.
Liu W, Han B, Zheng A, Zheng Z, Chen S, Jia S. Fault diagnosis of reducers based on digital twins and deep learning. Sci Rep. 2024;14:24406. https://doi.org/10.1038/s41598....
23.
Zhang W, Zhang T, Cui G, Pan Y. Dynamics analysis and deep learning-based fault diagnosis of defective rolling element bearing on the multi-joint robot. Machines. 2022;10(12):1215. https://doi.org/10.3390/machin....
24.
Rohan A, Raouf I, Kim HS. Rotate Vector (RV) Reducer fault detection and diagnosis system: towards component level prognostics and health management (PHM). Sensors. 2020;20(23):6845. https://doi.org/10.3390/s20236....
25.
Tu Z, Gao L, Wu X, Liu Y, Zhao Z. Rotate vector reducer fault diagnosis model based on EEMD-MPA-KELM. Appl Sci. 2023;13(7):4476. https://doi.org/10.3390/app130....
26.
Jaber AA, Bicker R. Fault diagnosis of industrial robot bearings based on discrete wavelet transform and artificial neural network. Int J Progn Health Manag. 2016;7(2). https://doi.org/10.36001/ijphm....
27.
Jaber AA, Bicker R. Industrial robot backlash fault diagnosis based on discrete wavelet transform and artificial neural network. Am J Mech Eng. 2016; 4(1):21-31. https://doi.org/10.12691/ajme-....
28.
Alobaidy MAA, Abdul-Jabbar JM, Aly M, Hassanpour R. Real time fault diagnosis in industrial robotics using discrete and slantlet wavelet transformations. Sci Rep. 2025;15:34145. https://doi.org/10.1038/s41598....
29.
Chen Z, Fu H, Zeng Z. A domain adaptation neural network for digital twin-supported fault diagnosis. In: International Conference on Control, Automation and Diagnosis (ICCAD). Piscataway: IEEE. 2025:1-6. https://doi.org/10.1109/ICCAD6....
30.
Ben Hnaien I, Gascard E, Simeu-Abazi Z, Dhouibi H. Fault diagnosis of industrial robots using a digital twin and GRU-Based Deep Learning. In: Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics. Setubal: SCITEPRESS. 2025:487-494. https://doi.org/10.5220/001370....
31.
Universal Robots. e-Series User Manual and RTDE Client Interface documentation. Odense: Universal Robots; 2024. Available from: https://docs.universal-robots.....
32.
Lindvig AP, Iturrate I, Kindler U, Sloth C. ur_rtde: An interface for controlling universal robots (UR) using the Real-Time Data Exchange (RTDE). In: 2025 IEEE/SICE International Symposium on System Integration (SII). Piscataway: IEEE; 2025: 1118-1123. https://doi.org/10.1109/SII593....
33.
International Organization for Standardization. ISO 9283:1998 Manipulating industrial robots - Performance criteria and related test methods. Geneva: ISO; 1998.
34.
Lynch KM, Park FC. Modern robotics: Mechanics, planning, and control. Cambridge: Cambridge University Press; 2017.
35.
Bittencourt AC, Axelsson P, Jung Y, Brogardh T. Modeling and identification of wear in a robot joint under temperature uncertainties. IFAC Proc Vol 2011;44(1):10293-10299. https://doi.org/10.3182/201108....
36.
MathWorks. Simscape Multibody Documentation. Natick: The MathWorks, Inc.; 2024. Available from: https://www.mathworks.com/help....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
