Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (6): 43-48.doi: 10.16180/j.cnki.issn1007-7820.2019.06.009

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Analysis of Discharge Parameters Affected by BP Neural Network Predicting Electrode Velocity

GUAN Sheng1,RUAN Fangming2,ZHOU Kui1,SU Ming2,WANG Heng2,DENG Di3,LI Jia4   

  1. 1. School of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China
    2. School of Big Data and Computer Sciences,Guizhou Normal University,Guiyang 550001,China
    3. Guizhou Machinery and Electronic Products Quality Supervision and Inspection Institute,Guiyang 550016,China
    4. Shenzhen Zhenhua Fu Electronics Limited Company,Shenzhen 518109,China
  • Received:2018-06-18 Online:2019-06-15 Published:2019-07-01
  • Supported by:
    Guizhou Province Electrostatic and Electromagnetic Protection Science and Technology Innovation Talent Team (Yankehe Platform Talent [2017] 5653);2016 Central Government Leading Local Science and Technology Development Special Fund Project (Zhongke Zhongdi [2016] No.4006)


The dual-process model of small gap discharge was used to investigate the influence of the change of field intensity and pressure on the internal correlation factors of the discharge gap, and the BP neural network was applied to predict and analyze the influence of the electrode moving speed on the discharge parameters. Based on the electrostatic discharge electrode moving speed effect detector, the electrode moving speed was continuously changed, and the discharge experiment had been repeated several times to obtain and statistically calculated the test data. Meanwhile, BP neural network was used to train and learn the measured experimental data, so as to predict the current rise time and peak current at different speeds and pressures. The results showed that there was no correlation between the rising time of the discharge current and the moving speed of the electrode. According to the new method, the accuracy of peak current at different speeds was higher than that of the actual size. The research results was of great reference value in exploring the law of non-contact electrostatic discharge and the formulation of electrostatic discharge standards.

Key words: electrostatic discharge, electrode movement speed, BP neural network, learning and training, prediction, disciplinary

CLC Number: 

  • TP183