基于HOSVD的工业机器人多关节轴承故障诊断分析  

Fault Diagnosis and Analysis of Industrial Robot Multi-Joint Bearing Based on HOSVD

作　　者：闫鸽 魏瑾 王少楠 Yan Ge;Wei Jin;Wang Shaonan(School of Mechanical and Electrical Engineering,Xi'an Jiaotong Engineering College,Xi'an Shaanxi 710300,China;China Energy Construction Northwest City Construction Co.,Ltd.,Xi'an Shaanxi 710300.China)

机构地区：[1]西安交通工程学院机械与电气工程学院,陕西西安710300 [2]中能建西北城市建设有限公司,陕西西安710300

出　　处：《现代工业经济和信息化》2024年第10期85-86,90,共3页Modern Industrial Economy and Informationization

摘　　要：工业机器人在运行过程中关节轴承承担着高频载荷运动,单一通道信号识别无法保障故障识别精度。为了进一步降低轴承多通道信号干扰,设计了一种基于截断高阶奇异值分解(HOSVD)的工业机器人多轴承故障诊断方法。研究结果表明:相较于原始信号,应用HOSVD处理后振动信号噪声完全除去,并且将脉冲特征有效保留下来,验证了本文设计方法是比较合理的。相比较单一轴承故障诊断结果,多轴承具有明显的优势,诊断精度具有99%以上,充分证明了多轴承融合模型的可行性。该研究有助于提高工业机器人的使用寿命,起到很好的节能效果。Industrial robots bear high-frequency load movements in joint bearings during operation,and single-channel signal identification cannot guarantee fault identification accuracy.In order to further reduce the interference of multi-channel signals of bearings,a multi-bearing fault diagnosis method based on truncated high-order singular value decomposition(HOSVD)for industrial robots is designed.The results show that compared with the original signal,the vibration signal noise is completely removed after applying HOSVD processing,and the pulse characteristics are effectively retained,which verifies that the design method in this paper is more reasonable.Compared with the single-bearing fault diagnosis results,multiple bearings have obvious advantages,and the diagnostic accuracy is more than 99%,which fully proves the feasibility of the multiple-bearing fusion model.This research helps to improve the service life of industrial robots and plays a good role in energy saving.

关 键 词：工业机器人轴承 多通道信号 故障诊断 截断高阶奇异值分解 

分 类 号：TH133[机械工程—机械制造及自动化]