基于电流信号的设备生产信息监控系统的开发

Yuguo Wang, Miaocong Shen, B. Han, Xiaochun Zhu, Jiaxiang Fei, Bin Xie
{"title":"基于电流信号的设备生产信息监控系统的开发","authors":"Yuguo Wang, Miaocong Shen, B. Han, Xiaochun Zhu, Jiaxiang Fei, Bin Xie","doi":"10.1109/cniot55862.2022.00020","DOIUrl":null,"url":null,"abstract":"Traditional machining equipment typically does not provide online production information such as part production quantities, efficiency and abnormal operating conditions. In order to solve this problem, a real-time production information monitoring system for traditional machining equipment based on electric current signal has been development. Firstly, the data acquisition hardware system using current sensors is designed to collect the electric current signal of the equipment being monitored. Next, the current data is processed by calibration algorithm to obtain production process feature vectors. Finally, a feature matching algorithm is used to identify the operating status. Based on the above algorithms, a monitoring software system is realized by C++ programming language on Qt platform. The monitoring experiment was carried out with automobile transmission shaft parts. The experimental results show that the machining start time and end time of each machined part are correctly and timely identified, and the abnormal state of the equipment could be accurately identified. The developed system is suitable for real-time monitoring of the traditional machining equipment.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of equipment production information monitoring system based on electric current signal\",\"authors\":\"Yuguo Wang, Miaocong Shen, B. Han, Xiaochun Zhu, Jiaxiang Fei, Bin Xie\",\"doi\":\"10.1109/cniot55862.2022.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional machining equipment typically does not provide online production information such as part production quantities, efficiency and abnormal operating conditions. In order to solve this problem, a real-time production information monitoring system for traditional machining equipment based on electric current signal has been development. Firstly, the data acquisition hardware system using current sensors is designed to collect the electric current signal of the equipment being monitored. Next, the current data is processed by calibration algorithm to obtain production process feature vectors. Finally, a feature matching algorithm is used to identify the operating status. Based on the above algorithms, a monitoring software system is realized by C++ programming language on Qt platform. The monitoring experiment was carried out with automobile transmission shaft parts. The experimental results show that the machining start time and end time of each machined part are correctly and timely identified, and the abnormal state of the equipment could be accurately identified. The developed system is suitable for real-time monitoring of the traditional machining equipment.\",\"PeriodicalId\":251734,\"journal\":{\"name\":\"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/cniot55862.2022.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/cniot55862.2022.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的机械加工设备通常不提供在线生产信息,如零件生产数量、效率和异常操作条件。为了解决这一问题,开发了一种基于电流信号的传统加工设备生产信息实时监控系统。首先,设计了基于电流传感器的数据采集硬件系统,采集被监测设备的电流信号。然后,通过标定算法对当前数据进行处理,得到生产过程特征向量。最后,采用特征匹配算法进行运行状态识别。基于上述算法,在Qt平台上用c++编程语言实现了监控软件系统。以汽车传动轴零件为对象进行了监测实验。实验结果表明,该方法能准确、及时地识别出各加工零件的加工开始时间和结束时间,并能准确识别出设备的异常状态。所开发的系统适用于传统加工设备的实时监控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Development of equipment production information monitoring system based on electric current signal
Traditional machining equipment typically does not provide online production information such as part production quantities, efficiency and abnormal operating conditions. In order to solve this problem, a real-time production information monitoring system for traditional machining equipment based on electric current signal has been development. Firstly, the data acquisition hardware system using current sensors is designed to collect the electric current signal of the equipment being monitored. Next, the current data is processed by calibration algorithm to obtain production process feature vectors. Finally, a feature matching algorithm is used to identify the operating status. Based on the above algorithms, a monitoring software system is realized by C++ programming language on Qt platform. The monitoring experiment was carried out with automobile transmission shaft parts. The experimental results show that the machining start time and end time of each machined part are correctly and timely identified, and the abnormal state of the equipment could be accurately identified. The developed system is suitable for real-time monitoring of the traditional machining equipment.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Statistical Power Grid Observability under Finite Blocklength Antenna On/Off Strategy for Massive MIMO Based on User Behavior Prediction A Residual Neural Network for Modulation Recognition of 24 kinds of Signals Intelligence Serviced Task-driven Network Architecture Novel Adaptive DNN Partitioning Method Based on Image-Stream Pipeline Inference between the Edge and Cloud
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1