伺服特征信息与RV减速器负载关联性研究
Study of Servo Characteristic Information and RV Reducer Loads Relevance
李恒 1赵兵 1赖泳辉 1张申1
作者信息
摘要
针对现有RV减速器工况识别中传感器安装位置受限和采集信号易受外界噪声干扰等问题,综合利用伺服特征信息对RV减速器工作状况进行监测,提高工业机器人在制造领域的服役性能.首先,根据RV减速器的结构参数及工作机制,分析输入转速与RV减速器关键频率、伺服特征信息与负载之间的关联性;然后,基于K-means聚类算法构建伺服特征信息与RV减速器负载之间的关联性辨识模型;最后,通过搭建RV减速器试验平台采集不同负载工况下伺服系统反馈信息,进行相应处理后运用关联辨识模型,实现了对负载状态的精确识别,识别率高达97.45%.本文可为基于伺服特征信息的RV减速器运行状态监测提供技术支撑.
Abstract
The sensor installation position is limited and the collected signal is easily disturbed by external noise in the existing RV reducer condition recognition.The comprehensive utilization of servo characteristic information to monitor the load condition of the RV reducer can improve the service performance of industrial robots in the field of manufacturing.Firstly,according to the structural parameters and working mechanism of the RV reducer,analyzed the correlation between the input speed and the important frequency,the servo characteristic information and the load of the RV reducer.Then,constructed the correlation identification model between servo feature information and RV reducer load based on the K-means clustering algorithm.Finally,the experimental platform of the RV reducer is built to collect the feedback information of the servo system under different load conditions.After corresponding processing,the correlation identification model is used to realize the accurate identification of load state,and the recognition rate is as high as 97.45%.This paper can provide technical support for monitoring the running state of RV reducer based on servo characteristic information.
关键词
工业机器人/特征信息/RV减速器/K-means聚类/负载/关联性Key words
Industrial robot/Characteristic information/RV reducer/K-means cluster/Load/Relevance引用本文复制引用
基金项目
青海省"昆仑英才·高端创新创业人才"计划(K9923194)
教育部产学研协同育人项目(202102108040)
青海大学智能制造工程创新实验班建设项目(RCPY-2021-04)
出版年
2024
NETL
NSTL
维普
万方数据