摘要
先进的故障诊断方法对保证工业机器人高效稳定运行具有重要作用。针对传统机器学习故障诊断的不足,利用模糊理论提高处理不确定信息的能力,构建一种协同模糊支持向量机(Synergetic Fuzzy Support Vector Machine,SFSVM)工业机器人故障诊断模型,并对其进行机制优化。在多论域空间结构下,综合处理工业机器人的不确定性信息运行状态监测数据和专家先验知识,提高了工业机器人故障诊断的适用性和鲁棒性。
Advanced fault diagnosis methods play an important role in ensuring the efficient and stable operation of industrial robots.In view of the shortcomings of the current fault diagnosis model of machine learning,with the benefits of fuzzy theory on uncertainty information,a novel fault diagnosis model based on Synergetic Fuzzy Support Vector Machine(SFSVM)and its optimization mechanism for industrial robot was proposed.It would achieve the synthetic analysis and processing of fuzzy and stochastic information and expert experience and knowledge in the operational condition monitoring based on the multi-scape structure of SFSVM and thus also improved the applicability and robustness of the fault diagnosis system for industrial robot.
作者
徐淑琼
袁从贵
甘伟
XU Shuqiong;YUAN Conggui;GAN Wei(School of Electronic Information,Dongguan Polytechnic,Dongguan 523808)
出处
《现代制造技术与装备》
2023年第3期196-199,共4页
Modern Manufacturing Technology and Equipment
基金
广东省教育厅普通高校特色创新类项目(2019GKTSCX138)
东莞职业技术学院院级基金(2021a1)
东莞职业技术学院智能终端及智能制造专项(ZXF001,ZXB003,ZXE004)。
