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
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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.
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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。
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3.70%
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