Fault Diagnosis of Industrial Robots Using a Digital Twin and GRU-Based Deep Learning

Ilhem Ben Hnaien, Eric Gascard, Zineb Simeu-Abazi, Hedi Dhouibi

2025

Abstract

This paper proposes a fault diagnosis method for industrial robots based on the combination of a digital twin and a GRU-based deep learning model. A high-fidelity digital replica of the 6-DOF Stäubli TX60 robot was developed using MATLAB Simulink and Simscape Multibody to simulate both normal and faulty behaviors. A dedicated fault injection module was used to generate motor blockage scenarios at different time instants, creating a labeled dataset of 49 classes. The time-series data were then used to train a Gated Recurrent Unit (GRU) neural network, which is efficient for modeling temporal patterns. The trained model achieved an accuracy of 87.35%, with strong performance across different fault types. This approach enables reliable, non-invasive, and repeatable fault diagnosis and provides a solid foundation for future work on predictive maintenance and deployment on real robotic platforms.

Paper Citation

in Harvard Style

Ben Hnaien I., Gascard E., Simeu-Abazi Z. and Dhouibi H. (2025). 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 - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 487-494. DOI: 10.5220/0013705600003982

in Bibtex Style

@conference{icinco25,
author={Ilhem Ben Hnaien and Eric Gascard and Zineb Simeu-Abazi and Hedi Dhouibi},
title={Fault Diagnosis of Industrial Robots Using a Digital Twin and GRU-Based Deep Learning},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={487-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013705600003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Fault Diagnosis of Industrial Robots Using a Digital Twin and GRU-Based Deep Learning
SN - 978-989-758-770-2
AU - Ben Hnaien I.
AU - Gascard E.
AU - Simeu-Abazi Z.
AU - Dhouibi H.
PY - 2025
SP - 487
EP - 494
DO - 10.5220/0013705600003982
PB - SciTePress