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Neural Network-Enhanced Fault Diagnosis of Robot Joints

Authors :
Yifan Zhang
Quanmin Zhu
Source :
Algorithms, Vol 16, Iss 10, p 489 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
19994893
Volume :
16
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Algorithms
Publication Type :
Academic Journal
Accession number :
edsdoj.6487c8b7ecd34dac9b7809dc3893548e
Document Type :
article
Full Text :
https://doi.org/10.3390/a16100489