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Neural network-enhanced fault diagnosis of robot joints

Zhang, Yifan; Zhu, Quanmin

Neural network-enhanced fault diagnosis of robot joints Thumbnail


Yifan Zhang

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Quan Zhu
Professor in Control Systems


Industrial robots play an indispensable role in flexible production lines, and the faults caused by degradation of equipment, motors, mechanical system joints, and even task diversity affect the efficiency of production lines and product quality. Aiming to achieve high-precision multiple size of fault diagnosis of robotic arms, this study presents a back propagation (BP) multiclassification neural network-based method for robotic arm fault diagnosis by taking feature fusion of position, attitude and acceleration of UR10 robotic arm end-effector to establish the database for neural network training. The new algorithm achieves an accuracy above 95% for fault diagnosis of each joint, and a diagnostic accuracy of up to 0.1 degree. It should be noted that the fault diagnosis algorithm can detect faults effectively in time, while avoiding complex mathematical operations.

Journal Article Type Article
Acceptance Date Oct 4, 2023
Online Publication Date Oct 20, 2023
Publication Date Oct 20, 2023
Deposit Date Nov 11, 2023
Publicly Available Date Nov 17, 2023
Journal Algorithms
Electronic ISSN 1999-4893
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 16
Issue 10
Article Number 489
Keywords Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis, Theoretical Computer Science
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