摘要
为了解决信息化条件下数控机床故障监测效率低、维修成本高、时延高等问题,同时满足网络技术、边缘计算、故障预测与健康管理技术相结合的智能诊断需求,提出基于信息物理系统的智能诊断系统。系统设计为4层:智能感知层、数据决策层、网络层、应用层。与传统的故障诊断与健康管理相比,该系统引入边缘计算技术与人工智能相关技术,解决系统的实时性、网络可靠性、数据安全性等问题,进一步实现智能化的故障预测与健康管理。以数控机床滚珠丝杠副为例,通过分析其故障现象并设计PHM流程,应用网络技术远程部署和配置PHM算法从而实现对机床的在线监测,同时能够识别丝杠的早期、中期和晚期故障。结果表明:该系统对进一步提高数控机床故障诊断可靠性以及实现设备智能运维和健康管理具有重要的意义。
In order to solve the problems of low fault monitoring efficiency,high maintenance cost and high delay of CNC machine tools under the condition of information technology,and meet the needs of intelligent diagnosis combining network technology,edge computing,fault prediction and health management technology,an intelligent diagnosis system based on the cyber physical system was proposed.The system was designed with four layers:intelligent sensing layer,data decision layer,network layer and application layer.Compared with the traditional fault diagnosis and health management,edge computing technology and artificial intelligence related technology were introduced to the system to solve the problems of real-time system,network reliability,data security and so on,and further to realize intelligent fault prediction and health management.Taking the ball screw pair of CNC machine tool as an example,by analyzing its fault phenomenon and designing prognostics and health management(PHM)process,the PHM algorithm was deployed and configured remotely by network technology,so as to realize online monitoring of the machine tool,and identify the early,middle and late faults of the ball screw.The results show that the system is of great significance to further improve the reliability of fault diagnosis of CNC machine tools and realize the intelligent operation and maintenance and health management of equipment.
作者
崔伟业
刘畅
杨琪
CUI Weiye;LIU Chang;YANG Qi(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming Yunnan 650504,China)
出处
《机床与液压》
北大核心
2023年第24期169-175,共7页
Machine Tool & Hydraulics
基金
云南省重大科技专项计划(202102AC080002)。
关键词
信息物理系统
数控机床
边缘计算
故障预测与健康管理
智能诊断
Cyber physical system
CNC machine
Edge computing
Prognostics and health management(PHM)
Intelligent diagnosis
