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

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.