基于云平台的锻压机床状态监测与故障诊断系统研究  

Research on Status Monitoring and Fault Diagnosis System of Forging Machine Based on Cloud Platform

作　　者：赵文兵[1] 张春雨 夏怡[1] ZHAO Wenbing;ZHANG Chunyu;XIA Yi(School of Elecerical Engineering,Changzhou Vocational Institute of Mechatronic Technology,Changzhou Jiangsu 213164,China;College of Mechanical Engineering,Anhui Science and Technology University,Chuzhou Anhui 233100,China)

机构地区：[1]常州机电职业技术学院电气工程学院,江苏常州213164 [2]安徽科技学院机械工程学院,安徽滁州233100

出　　处：《机床与液压》2022年第22期172-178,共7页Machine Tool & Hydraulics

基　　金：安徽省科技攻关(1604a0902134);江苏省高等学校大学生创新创业训练计划项目(202013114016Y)。

摘　　要：研究基于云平台的状态检测与故障诊断系统方案,提出基于NETWORX和DJANGO双软件架构的策略,以解决监控评价和故障诊断网络融合的问题及实现监控多台设备的目的。设计以PLC为核心的现场控制系统;NETWORX架构可以方便与各种物联网采集系统交换数据,所以用NETWORX架构实现云平台的远程监控程序;采用Python的DJANGO设计状态检测和故障诊断程序。结果表明:所提系统特征参数的采集精度在1%范围内,控制及显示监控功能都符合设计要求。利用经验故障数据对分类回归故障树(CART)、SVM、MLP 3种常见的故障诊断智能算法进行比较。结果表明:CART算法、SVM算法、MLP算法的故障诊断正确率分别为91.3%、73.2%、86.2%,证明基于云平台的锻压机床状态监测与故障诊断系统能够满足设计需要。The scheme of condition detection and fault diagnosis system based on cloud platform was studied, and the strategy of dual software architecture based on NETWORX and DJANGO was proposed to solve the problem of network integration of monitoring evaluation and fault diagnosis, and to realize the purpose of monitoring multiple devices.The field control system with PLC as the core was designed;the NETWORX architecture could easily exchange data with various internet of things acquisition systems, so it was used to realize the remote monitoring program of cloud platform;the programs of state detection and fault diagnosis were designed by using DJANGO of Python.The results show that the acquisition accuracy of the proposed system characteristic parameters is within 1%,and the control and display monitoring functions both meet the design requirements.The intelligent algorithms of classification and regression trees(CART),SVM and MLP were compared with the experience fault data.The results show that the fault correct rate of CART algorithm, SVM algorithm and MLP algorithm is 91.3%,73.2% and 86.2% respectively, which proves that the state monitoring and fault diagnosis system based on cloud platform can meet the design needs.

关 键 词：锻压机床 状态检测 故障诊断 云平台 智能算法 

分 类 号：TH17[机械工程—机械制造及自动化]