Neural Network-Enhanced Fault Diagnosis of Robot Joints

被引:1
|
作者
Zhang, Yifan [1 ]
Zhu, Quanmin [2 ]
机构
[1] Univ Bristol, Sch Engn Math, Colston Ave 35, Bristol BS1 4TT, Avon, England
[2] Univ West England, Sch Engn, Frenchay Campus,Coldharbour Lane, Bristol BS16 1QY, Avon, England
关键词
fault diagnosis; BP neural network; feature extraction; data fusion; MANIPULATORS;
D O I
10.3390/a16100489
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:21
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