基于可信标识和数据驱动的工业设备运维系统构建方法研究  

Research on Construction Method of Industrial Equipment Operation and Maintenance System Based on Trusted Identification and Data Driven

作　　者：常赛科 孙文磊[1] 刘志远 罗浩 路程 江伦 巴胤竣 姜任奔 CHANG Saike;SUN Wenlei;LIU Zhiyuan;LUO Hao;LU Cheng;JIANG Lun;BA Yinjun;JIANG Renben(College of Intelligent Manufacturing Modern Industry,Xinjiang University,Urumqi Xinjiang 830049,China)

机构地区：[1]新疆大学智能制造现代产业学院,新疆乌鲁木齐830049

出　　处：《机床与液压》2025年第5期166-175,共10页Machine Tool & Hydraulics

基　　金：国家工信部重点项目(TC210A02E);自治区重点研发项目(2022B01049)。

摘　　要：为满足工业设备数字化运维需求,通过总结工业设备智能运维现状,以油田抽油机为研究对象,构建一个工业设备运维管理系统。详细阐述构建系统所用的关键技术,分别采用AlexNet、Vgg16、GoogleNet 3种不同的卷积神经网络故障诊断模型对油田抽油机示功图数据进行诊断识别,总体分类准确率分别为94.7%、94.6%、95.6%,分类效果良好。为实现设备工况的准确识别,结合工业互联网标识解析技术提出一种基于物元可拓的故障诊断模型与故障预警推理模型。最后,搭建工业设备运维系统小程序端,实现了设备故障信息快速查询与上传,实现了设备数字化运维。In order to meet the demand for digital operation and maintenance of industrial equipment,an industrial equipment operation and maintenance management system was constructed by summarizing the current situation of intelligent operation and maintenance of industrial equipment and taking the oilfield pumping machine as the research object.The key technologies used in the construction of the system were elaborated in detail.Three kinds of convolutional neural network fault diagnosis models,AlexNet,Vgg16 and GoogleNet,were used to diagnose and identify the data of oilfield pumping machine indicator diagrams.The overall classification accuracy is 94.7%,94.6%,95.6%,respectively,and the classification effect is good.In order to realize the accurate identification of the equipment conditions,a fault diagnosis and early warning inference model based on matter-element extension were proposed in combination with industrial internet identity resolution technology.Finally,the small program end of industrial equipment operation and maintenance system was built,by which the rapid query and upload of equipment fault information was realized.So the digital operation and maintenance of equipment is realized.

关 键 词：标识解析 设备运维 故障诊断 物元可拓 

分 类 号：TH17[机械工程—机械制造及自动化] TP27[自动化与计算机技术—检测技术与自动化装置]