Vision-Based Fault Classification for Monitoring Industrial Robot
作者单位：College of Electronics and Information Engineering University of Science and Technology Liaoning Lingyuan Steel Co LTD Anshan City Land Resources Survey and Design Institute
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化]
基 金：supported by Youth Fund of University of Science and Technology Liaoning under Grant 2012QN15
摘 要：In this paper, the sparse optical flow（SOF）, in form of three-way array, is used as motion *** on unfolded SOF, we apply PCA to get the principal component（PC）. Then, two statistics T2 and SPE are defined for monitoring the motion process of the industrial robot. To improve the performance of the fault detection accuracy, we apply multi-manifold learning algorithm with the labeled root fault area frame detected by PCA *** proposed method can detect the fault more effectively, while reducing the false alarm rate significantly. Experiment of robotic-arm based marking system（RABMS） is taken to evaluate the performance of the proposed method. The results demonstrate the capability of the proposed methods.