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案例频道作者：北京和利时智能技术有限公司，宁波和利时智能科技有限公司 范莹，李天辉，刘宗福，姜百宁
摘要：为解决机器人故障预测问题，提出一种使用机器人轴温预测的方法，实现机器人异常发热的提前预警，避免故障的发生。本文使用生产中机器人的历史数据作为数据源，分析对机器人轴温产生影响的测点，结合机器人机理，对数据进行衍生转换等操作，生成可以用于机器人轴温预测的特征数据。鉴于没有免费午餐（NFL，No Free Lunch）定理，对所有数据可用回归算法进行自动化选择，然后使用hyperopt进行优化调参，最终生成可用的机器人轴温预测模型。本文中所述数据获取、分析建模、上线部署及在线预测全部过程在HolliCube工业互联网平台上进行。
关键词：温度预测；故障诊断；机器人；工业互联网平台；运行周期
Abstract: Aiming at fault diagnosis for robots, a method which predicts robot's axes temperature is proposed to realize abnormal robot heating in advance and avoid the occurrence of faults. In this paper, the historical data of the robot in production are used as data source to analyze the measuring points which affect the axle temperature of the robot. Combined with the mechanism of the robot, the data are derived and converted to generate the characteristic data which can be used to predict the axle temperature of the robot. Then select regression algorithms automatically caused by the NFL theory, and then optimize the hyper-parameters by hyperopt and finally we got the model. The whole process of data acquisition, analysis and modeling, online deployment and online prediction described in this paper are carried out on HolliCube industrial Internet platform.
Key words: Temperature prediction; Fault diagnosis; Robot;Industrial internet platform; Running cycle
在线预览：基于数据驱动的机器人轴温预测建模与应用
摘自《自动化博览》2019年10月刊
北京市公安局海淀分局备案号：11010802023656号