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Fault diagnosis of robot joint based on BP neural network

Published online by Cambridge University Press:  22 July 2022

Ming Hu ,
Jianguo Wu Open the ORCID record for Jianguo Wu [Opens in a new window] ,
Jing Yang Open the ORCID record for Jing Yang [Opens in a new window] ,
Lijian Zhang  and
Fan Yang
Ming Hu
Affiliation:
Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China
Jianguo Wu
Affiliation:
Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China
Jing Yang*
Affiliation:
Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China
Lijian Zhang
Affiliation:
Beijing Institute of Spacecraft Environmental Engineering, Beijing, China
Fan Yang
Affiliation:
Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.

Keywords

robotBP Neural networkjoint fault diagnosisend poseangle error
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4388 - 4404
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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