A Noncontact Method for Measuring the Charge of a Moving Object Based on Mutual Capacitance Matrix

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2025-01-07 DOI:10.1109/JSEN.2024.3524277
Zhongzheng He;Sichao Qin;Juan Wu;Yu Qiao;Pengfei Li;Xi Chen
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Abstract

The charge quantity is a fundamental physical parameter that reflects the electrical state of an object. Accurately estimating the charge of an object facilitates the assessment of electrostatic discharge risks and aids in preventing accidents. Measuring the charge of a moving object has long posed a technical challenge in this field. This article proposes a noncontact method for estimating the charge of a moving object by using the electrostatic signals generated by the object’s motion and its motion data. First, a noncontact charge measurement model based on a mutual capacitance matrix was developed using the image charge method in electrostatics. The accuracy of the model was verified through simulations of the charge on the sensing electrode. Next, a correction method for charge calculation was further proposed to reduce measurement errors caused by parasitic capacitance from the experimental setup. Finally, a verification experiment was conducted, wherein an electrometer measured the charge of the object in a stationary state, providing a reference to validate the proposed method. The experimental results demonstrated a strong correlation ( ${r}~\gt 0.96$ ) and consistency (within the 95% confidence interval) between the measured and reference values across various conditions. The absolute error of the measurements was within ±1 nC (mean ± standard deviation: $- 0.04~\pm ~0.4$ nC), with a relative error of approximately ±10%. This study contributes to the prevention of electrostatic discharge accidents involving moving objects and presents novel insights and technological approaches for electrostatic detection.
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基于互容矩阵的非接触运动物体电荷测量方法
电荷量是反映物体电学状态的基本物理参数。准确估算物体的电荷,有利于评估静电放电的风险,有助于防止事故的发生。长期以来,测量运动物体的电荷一直是这一领域的技术难题。本文提出了一种利用运动物体运动产生的静电信号及其运动数据来估计运动物体电荷的非接触方法。首先,利用静电学中的图像电荷法建立了基于互电容矩阵的非接触电荷测量模型。通过对传感电极上电荷的模拟,验证了该模型的准确性。其次,提出了一种电荷计算的修正方法,以减小实验装置中寄生电容带来的测量误差。最后,进行了验证实验,静电计测量了静止状态下物体的电荷,为验证所提出的方法提供了参考。实验结果表明,在各种条件下,实测值与参考值之间具有很强的相关性(${r}~\gt 0.96$)和一致性(在95%置信区间内)。测量结果的绝对误差在±1 nC以内(平均值±标准差:$- 0.04~\pm ~0.4$ nC),相对误差约为±10%。这项研究有助于预防涉及运动物体的静电放电事故,并为静电检测提供了新的见解和技术方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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