Abstract：For the actual engineering industrial robot end-jitter degree is difficult to be effectively distinguished by simple indicators, and the recognition process exists large external interference and uneven distribution of jitter position problems, an industrial robot end-jitter state recognition method with variable initial position data augmentation and deep feature extraction is proposed. Firstly, power-line interference filtering in industrial robot end vibration signals using trap filters, and by means of root mean square threshold search jitter location and changing the search initial position data enhancement, to obtain sufficient end-jitter data that can show the motion state of the industrial robot; Secondly, the end-jitter data is decomposed by continuous wavelet transform to obtain the time-frequency map that can fully retain the end jitter shock and vibration characteristics; Finally, to mitigate the effects of feature downscaling and work condition variation, a convolutional neural network with the pooling layer removed and batch normalization added is applied to perform deep feature extraction and classification of the time-frequency map, which enables industrial robot end-jitter state recognition. The experimental results show that the proposed method achieves an accuracy of more than 90% in different sensor acquisition directions, which proves that the method can effectively recognition the end jitter state of industrial robots and has good generalization and stability.
陈仁祥1，谢文举1，杨宝军2，胡小林3，潘升1. 变初位数据增强和深度特征提取的工业机器人末端抖动状态识别[J]. 振动与冲击, 2023, 42(11): 199-206.
CHEN Renxiang1, XIE Wenju1, YANG Baojun2, HU Xiaolin3, PAN Sheng1. End-jitter state recognition of industrial robots with variable initial position data augmentation and deep feature extraction. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(11): 199-206.
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