面向智能制造的工业机器人健康评估方法  

An Industrial Robot Health Assessment Method for Intelligent Manufacturing

作　　者：赵威[1,2,3,4] 王锴 徐皑冬[1,2,3] 曾鹏[1,2,3] 杨顺昆[5] 孙越[1,2,3,4] 郭海丰[1,2,3,4] ZHAO Wei;WANG Kai;XU Aidong;ZENG Peng;YANG Shunkun;SUN Yue;GUO Haifeng(Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Shenyang 110016,China;State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]中国科学院网络化控制系统重点实验室,辽宁沈阳110016 [2]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [3]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169 [4]中国科学院大学,北京100049 [5]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《机器人》2020年第4期460-468,共9页Robot

基　　金：国家重点研发计划(2017YFE0123000)。

摘　　要：以智能制造领域中最具有代表性的设备——工业机器人——为研究对象,针对其精度退化及设备故障问题,研究了工业机器人健康评估问题.首先,对工业机器人核心部件进行了失效模式及影响分析,并对现有工业机器人健康评估方法进行了综述.其次,提出了基于边-云协同和深度学习的工业机器人健康评估框架.在边缘层应用基于群组聚类和对等比较的方法进行异常检测,快速识别出发生异常的设备;在云端借助故障预测与健康管理技术以及人工智能算法对发生异常的设备进行深度健康评估.最后,对基于深度学习的工业机器人健康评估方法进行了展望.The health assessment methods are studied for industrial robots, which are the most representative equipments in the field of intelligent manufacturing, to address the issues of their accuracy degradation and equipment failure. Firstly,the failure modes and their effects of the core components of industrial robots are analyzed, and the existing industrial robot health assessment methods are reviewed. Secondly, a health assessment framework of industrial robot based on cloudedge collaboration and deep learning is proposed. At the edge layer, an anomaly detection method based on fleet clustering and peer-to-peer comparison is applied to detecting abnormal devices quickly. At the cloud layer, prognostics and health management with the artificial intelligence algorithms are used to perform deep health assessment on abnormal devices.Finally, the health assessment method of industrial robot based on deep learning is prospected.

关 键 词：智能制造 故障预测与健康管理 工业机器人 边缘计算 深度学习 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]