基于电流信号的机器人关节螺栓松动故障检测

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (6) : 248-254.

论文

杨家淞1，柳小勤1，徐凯1，王东晓1，伍星1，2

作者信息 +

Fault detection of robot joint bolt loosening based on electrical current signal

YANG Jiasong1, LIU Xiaoqin1, XU Kai1, WANG Dongxiao1, WU Xing1，2

Author information +

文章历史 +

摘要

针对工业机器人关节连接处螺栓松动问题，根据机器人关节结构建立机电耦合模型，然后引入了底座松动，建立了螺栓松动的系统动力学模型。分析该仿真模型电流信号，得出电流信号与螺栓松动故障之间规律。研究表明，松动情况下电机电流频率附近出现边频带，时域波形出现脉冲冲击，时频图上出现能量波动，冲击与波动出现的时刻对应螺栓松动时刻。所提出的模型分析结果与试验数据吻合较好，研究成果对工业机器人关节螺栓松动故障的建模及诊断研究具有重要的参考价值。

Abstract

Aiming at the problem of bolt loosening at the joints of industrial robots, an electromechanical coupling model was established according to the robot joint structure, and then the base loosening was introduced to establish the system dynamics model of bolt loosening. The current signal of the simulation model is analyzed, and the law between the current signal and the bolt loosening fault is obtained. The research shows that in the case of loosening, sidebands appear near the current frequency of the motor, pulse shocks appear in the time-domain waveform, and energy fluctuations appear on the time-frequency diagram. The analysis results of the proposed model are in good agreement with the experimental data, and the research results have important reference value for the modeling and diagnosis of industrial robot joint bolt loosening faults.

导出引用

杨家淞1，柳小勤1，徐凯1，王东晓1，伍星1，2. 基于电流信号的机器人关节螺栓松动故障检测[J]. 振动与冲击, 2023, 42(6): 248-254

YANG Jiasong1, LIU Xiaoqin1, XU Kai1, WANG Dongxiao1, WU Xing1，2. Fault detection of robot joint bolt loosening based on electrical current signal[J]. Journal of Vibration and Shock, 2023, 42(6): 248-254

参考文献

[1] 缑百勇, 陆秋海, 王波等. 利用固有频率异常值分析法检测螺栓拧紧力[J]. 振动与冲击, 2015, 34(23): 77-82.
Jin Baiyong, Lu Qiuhai, Wang Bo, et al. Detecting bolt tightening force by natural frequency outlier analysis method[J]. Journal of Vibration and Shock, 2015, 34(23): 77-82.
[2] 郭洁, 陈祥献, 黄海等. 基于峰度的电力变压器铁芯松动故障在线监测方法[J]. 仪器仪表学报, 2010, 31(11): 2401-2407.
Guo Jie,Chen Xiangxian,Huang Hai,Lin Rilei,Zhou Caikang.On-line monitoring method for power transformer core looseness fault based on kurtosis[J].Chinese Journal of Scientific Instrument,2010,31(11):2401-2407.
[3] 胡业林, 代斌, 宋晓. 基于小波包和AFSA-SVM的电机故障诊断[J]. 电子测量技术, 2021, 44(02): 48-55.
Hu Yelin,Dai Bin,Song Xiao.Motor fault diagnosis based on wavelet packet and AFSA-SVM[J].Electronic Measurement Technology,2021,44(02):48-55.
[4] 邵俊华, 王涛, 汪正傲等. 基于压电阻抗频率变化的螺栓松动检测技术[J]. 中国机械工程, 2019, 30(12): 1395-1399+1408.
Shao Junhua,Wang Tao,Wang Zhengao,Wei Denglai,Li Yourong.Bolt looseness detection technology based on frequency change of piezoelectric impedance[J].China Mechanical Engineering,2019,30(12):1395-1399+1408.
[5] Dong G, Chen J. Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2012, 33: 212-236.
[6] 董广明, 陈进, 雷宣扬等. 基于小波包-神经网络方法的支撑座连接螺栓松动损伤诊断的实验研究[J]. 机械科学与技术, 2006(01): 102-106.
Dong Guangming, Chen Jin, et al. Experimental research on the diagnosis of loosening and damage of supporting seat connecting bolts based on wavelet packet neural network method[J]. Mechanical Science and Technology, 2006, 25 (1): 102 ~106
[7] 孙卫祥, 陈进, 伍星等. 基于信息融合的支撑座早期松动故障诊断[J]. 上海交通大学学报, 2006(02): 239-242+247.
Sun Weixiang, Chen Jin et al. Early failure diagnosis of support seat loosening based on information fusion[J]. Shanghai Jiaotong University Journal, 2006, 40 (2): 123 ~126
[8] 闫航瑞, 曾国英, 赵登峰等. 基于小波分析的螺栓联接松动故障诊断[J]. 机械科学与技术, 2012, 31(07): 1110-1114.
Yan Hangrui,Zeng Guoying,Zhao Dengfeng,Tian Meizi. Fault diagnosis of the attachment bolt looseness based on wavelet analysis [J].Mechanical Science and Technology, 2012, 31(7): 1110-1114.
[9] 李允公, 孔祥娜, 高玉勇. 基于两被联件振动信号概率密度和PCA的螺栓松动识别方法研究[J]. 振动与冲击, 2015, 34(01): 63-67.
Li Yungong, Kong Xiangna, Gao Yuyong. Research on Bolt Loose Recognition Method Based on Probability Density of Vibration Signal of Two Coupled Parts and PCA[J]. Journal of Vibration and Shock, 2015, 34(01): 63-67.
[10] 谢锋云, 刘昆, 冯春雨等. 基于VMD的螺栓松动状态识别（英文）[J]. 机床与液压, 2020, 48(06): 61-65+130.
Xie Fengyun,Fu Yu,Liu Kun,Xiao Qian,Liu Haitao.Research on bolt looseness detection method based on multi-domain features[J].Journal of Electronic Measurement and Instrument,2021,35(04):109-117.
[11] Huda F, Kajiwara I, Hosoya N, et al. Bolt loosening analysis and diagnosis by non-contact laser excitation vibration tests[J]. Mechanical Systems and Signal Processing, 2013, 40(2): 589-604.
[12] Hui M, Xueyan Z, Yunnan T, et al. Analysis of Dynamic Characteristics for a Rotor System with Pedestal Looseness[J]. Shock and Vibration, 2011, 18(1-2).
[13] Ma H, Huang J, Zhang S Y, et al. Nonlinear vibration characteristics of a rotor system with pedestal looseness fault under different loading conditions[J]. Journal of Vibroengineering, 2013, 15(1): 406-418.
[14] Jiang M, Wu J, Peng X, et al. Nonlinearity measure based assessment method for pedestal looseness of bearing-rotor systems[J]. Journal of Sound and Vibration, 2017, 411: 232-246.
[15] Yang Y, Yang Y, Cao D, et al. Response evaluation of imbalance-rub-pedestal looseness coupling fault on a geometrically nonlinear rotor system[J]. Mechanical Systems and Signal Processing, 2019, 118: 423-442.
[16] 白种桐. 破碎机电机故障在线诊断技术[J]. 甘肃科技, 2009, 25(08): 76-78.
Bai Zhongtong. On-line diagnosis technology of crusher motor faults[J]. Gansu Science and Technology,2009,25(8):76-78. DOI:10.3969/j.issn.1000-0952.2009.08.027.
[17] 呼守信. 基于模型的电动机驱动设备在线故障诊断[J]. 煤矿机电, 2014(04): 22-23+28.
Hu Shouxin. Model-based online fault diagnosis of motor drive equipment[J]. Coal Mine Electromechanical,2014(4):22-23,28. DOI:10.3969/j.issn.1001-0874.2014.04.007.
[18] 郑佳, 修立双. 不同生弧原因下串联型故障电弧实验研究[J]. 电源学报, 2021, 19(02): 121-127.
Zheng Jia,Xiu Lishuang.Experimental research on series fault arc under different arc generation reasons[J].Journal of Power Supply,2021,19(2):121-127.
[19] Corne B, Vervisch B, Derammelaere S, et al. The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines[J]. Mechanical Systems and Signal Processing, 2018, 107: 168-182.
[20] Cabal-Yepez E, Romero-Troncoso R J, Garcia-Perez A, et al. Single-parameter fault identification through information entropy analysis at the startup-transient current in induction motors[J]. Electric Power Systems Research, 2012, 89: 64-69.
[21] Gu F, Shao Y, Hu N, et al. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment[J]. Mechanical Systems and Signal Processing, 2011, 25(1): 360-372.
[22] Zhen D, Wang T, Gu F, et al. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping[J]. Mechanical Systems and Signal Processing, 2013, 34(1-2): 191-202.
[23] 段吉安, 黄志开. 旋转机械松动故障的非线性力学模型[J]. 中南工业大学学报(自然科学版), 2002(01): 78-81.
Duan Ji'an,Huang Zhikai.Non-linear mechanics model of rotating machinery loosening failure[J].Journal of Central South University of Technology (Natural Science Edition),2002(01):78-81.