基于电磁场的非侵入式设备在线监控与预警系统
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摘要
利用物联网能够将信息感知技术、网络技术、智能运算技术融为一体,完成设备健康状态信息的实时协同采集、智能处理、及时反馈等功能。工业设备大部分都是电器设备,通过建立电气设备运行过程中感应磁场波形与设备运行状态之间的对应关系,实现一种通用的非侵入式设备在线监控与预警系统。该系统已在中国商飞部署,帮助中国商飞实现透明工厂。
By using the Internet of Things, it is possible to integrate information perception technology, network technology and intelligent computing technology to achieve the functions of real-time collaborative acquisition, intelligent processing and timely feedback of equipment health status information. Industrial equipment is mostly electric equipment. By establishing the corresponding relationship between the waveform of induction magnetic field and the operation status of the equipment, a general non-intrusive equipment online monitoring and early warning system is realized. The system has been deployed in China Merchant Airlines to help it achieve transparent factories.
By using the Internet of Things, it is possible to integrate information perception technology, network technology and intelligent computing technology to achieve the functions of real-time collaborative acquisition, intelligent processing and timely feedback of equipment health status information. Industrial equipment is mostly electric equipment. By establishing the corresponding relationship between the waveform of induction magnetic field and the operation status of the equipment, a general non-intrusive equipment online monitoring and early warning system is realized. The system has been deployed in China Merchant Airlines to help it achieve transparent factories.
引文
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[11] 黄宜华.大数据机器学习系统研究进展[J].大数据,2015,1(1):28-47.
[2] 李红卫,杨东升,孙一兰.智能故障诊断技术研究综述与展望[J].计算机工程与设计,2013,34(2):632-637.
[3] 孙小江,李键,朱勇.物联网技术在小水电站在线监测及故障诊断的应用[J].电气设计,2017,10(A):61-62.
[4] Wichakool W,Remscrim Z,Orji U A,at al.Smart Metering of Variable Power Loads[J].IEEE Transactions on Smart Grid,2015,6(1):189-198.
[5] Changhe S,Yumei W,Ping L,et al.Self-Contained Wireless Hall Current Sensor Applied for Two-Wire Zip-Cords[J].IEEE Transactions On Magnetics,201652(7):1-4.
[6] 彭宇,刘大同,彭喜元.故障预测与健康管理技术综述[J].电子测量与仪器学报,2010(1):1-9.
[7] 孙冬.基于混合模型的故障检测与诊断方法的研究与应用[D].南京:南京航空航天大学,2013.
[8] 刘强,柴天佑,秦泗钊.基于数据和知识的工业过程监视及故障诊断综述[J].控制与决策,2010,25(6):801-807.
[9] Alag S,Agogino A M,Morjaria M.A methodology for intelligent sensor measurement,validation,fusion,and fault detection for equipment monitoring and diagnostics[J].Artificial Intelligence for Engineering Design,Analysis and Manufacturing,2001,15(415):307-320.
[10] 刘达新.基于运行大数据学习的复杂装备故障诊断技术及其典型应用[J].中兴通讯技术,2017,23(4):56-59.
[11] 黄宜华.大数据机器学习系统研究进展[J].大数据,2015,1(1):28-47.