MFD: Multi-object Frequency Feature Recognition and State Detection Based on RFID-single Tag

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-08-24 DOI:10.1145/3615665
Biaokai Zhu, Zejiao Yang, Yupeng Jia, Shengxin Chen, Jie Song, Sanman Liu, P. Li, Feng Li, Deng-ao Li
{"title":"MFD: Multi-object Frequency Feature Recognition and State Detection Based on RFID-single Tag","authors":"Biaokai Zhu, Zejiao Yang, Yupeng Jia, Shengxin Chen, Jie Song, Sanman Liu, P. Li, Feng Li, Deng-ao Li","doi":"10.1145/3615665","DOIUrl":null,"url":null,"abstract":"Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this paper, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"21 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3615665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this paper, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.
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
基于rfid单标签的多目标频率特征识别与状态检测
振动是机械运行过程中发生的一种正常反应,在工业系统中很常见。如何将细粒度的振动感知转化为可视化,并基于视觉振动信息进一步预测机械故障,减少财产损失,这引起了我们的思考。本文对标签产生的相位信息进行处理和分析,提出了一种实时振动监测与故障感知识别系统MFD。MFD从原始射频信号中提取相位信息,通过引入高斯白噪声和低通滤波器进行降噪,将其转换成马尔可夫转换图。为准确预测机械故障,引入深度机器学习模型计算故障分析精度,实现实时监测和故障判断。试验结果表明,该系统对振动的平均识别精度可达96.07%,对电机设备长期运行时的正转、反转、溢油和螺钉松动的平均识别精度分别可达98.53%、99.44%、97.87%和99.91%,具有较高的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
期刊最新文献
Introduction to the Special Issue on Wireless Sensing for IoT Special Issue on Wireless Sensing for IoT: A Word from the Editor-in-Chief Resilient Intermediary‐Based Key Exchange Protocol for IoT A Two-Mode, Adaptive Security Framework for Smart Home Security Applications Online learning for dynamic impending collision prediction using FMCW radar
×
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