摘要
针对目前数控机床智能监控水平较低、智能化不足的问题,提出基于5G通信的数控机床智能监控与故障诊断系统。该系统以高性能STM32为硬件核心,搭载AliOS Things嵌入式操作系统实现机床电机工作电流、电压、温度和振动信号的实时监测;再提取信号时频域特征,进行故障状态识别,通过5G通信智能网关将异常状态数据上传到远程智能监控服务平台。该平台还能下发指令给监控系统,提示并辅助工作人员对存在故障风险的数控机床进行及时维护。经测试,该系统能够实现高并发海量数据的采集与高速传输,5G通信传输速度在500 Mb/s,传输时延在20 ms以内,平均数据丢包率在2%内。测试结果表明该系统性能稳定可靠,在满足数控机床智能监控同时,为数控机床设备的智能升级提供技术方案。
In view of the low level of intelligent monitoring and insufficient intelligence of CNC machine tools at present,the intelligent monitoring and fault diagnosis system for CNC machine tools based on 5G communication was proposed.The ultra-low power STM32 was used as the hardware core,and was equipped with the AliOS Things embedded operating system to realize monitoring of the working current,voltage,temperature and vibration signals of the machine tool motor real-time.Then the time-frequency domain features of the signal were extracted to realize fault status identification,and the abnormal status data were uploaded to the remote intelligent monitoring service platform by using the 5G communication intelligent gateway.Meanwhile,the platform can issue instructions to the monitoring system,prompt and assist staff to timely maintenance the CNC machine tools with fault risky,which can ensure the reliable and stable operation of CNC machine tools.After testing,the monitoring system can achieve high-concurrency and massive data collection and high-speed transmission.The 5G communication transmission speed is 500 Mb/s,the transmission delay is within 20 ms,the average data packet loss rate is within 2%.The test results show that the system has stable and reliable performance,which not only meets the intelligent monitoring of CNC machine tools,but also provides technical solutions for the intelligent upgrade of CNC machine tools.
作者
冯超
张帝
FENG Chao;ZHANG Di(Taizhou Vocational College of Science&Technology,Taizhou Zhejiang 318020,China;School of Electrical and Electronic Engineering,Anhui Science and Technology University,Bengbu Anhui 233100,China)
出处
《机床与液压》
北大核心
2023年第12期142-150,共9页
Machine Tool & Hydraulics
基金
浙江省“十三五”第二批教学改革研究项目(jg20191003)
浙江省教育厅2022年度高校国内访问工程师校企合作项目(FG2022380)
安徽省教育厅重点项目(KJ2019A0803)
安徽科技学院人才引进项目(DQYJ201902)。
