基于LSTM预测模型的电极速度对放电参数影响分析

X. He, Fangming Ruan, Lan Yin, Yanli Chen, Yaru Yang, Xiao Wang
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引用次数: 0

摘要

讨论了放电电流与电极移动速度、电离系数、场强、气体压力、温度、湿度等因素的关系。根据空气动力学中的伯努利定律,分析了电极向目标移动过程中电极周围气体的流动分布。分析了放电间隙低真空局部零件形成的机理。用气体电离雪崩过程和表面电子发射过程两个子过程来描述小间隙放电。利用Paschen定律,分析了电极向目标移动速度对放电电流的影响。在静电放电电极移动速度效应测试平台上进行了多次实验,并记录了实验数据。利用长短期记忆网络(LSTM)模型对实验数据进行训练和学习,预测不同电极移动速度和上升时间下的放电电流峰值。实验结果表明,电极移动速度与放电峰值电流有较强的相关性,而电极移动速度与电流上升时间有一定的相关性,但相对较弱。研究结果对非接触静电放电标准的提出和制定具有一定的参考意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Discharge Parameters Influenced by Electrode Velocity Based on LSTM Prediction Model
Relationships were discussed in this work between discharge current and electrode moving speed, ionization coefficient, field strength, gas pressure, temperature, humidity and other factors. Gas flow distribution around electrode, according to Bernoulli's law in aerodynamics, was analyzed during the electrode moving toward the target. Mechanism was explained on forming of local part low vacuum in the discharge gap. Two sub-processes of gas ionization avalanche process and surface electronic emission process were used to describe small gap discharge. With Paschen's law, analysis of influence was performed on discharge current by electrode moving speed toward the target. Experiments were conducted many times based on test platform of electrostatic discharge electrode moving speed effect, while experiment data were recorded. Long Short-Term Memory network (LSTM) model was used to train and learn experimental data, predicted peak value of discharge current at different electrode moving speeds and rise time. The experimental results show that there is a strong correlation between the electrode moving speed and the peak discharge current, while there is a certain correlation between the electrode moving speed and the current rise time, but it is relatively weak. The research results maybe have some reference significance for the proposal and formulation of non-contact electrostatic discharge standards.
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