Experimental Characterization and Data Analysis of Electrostatic Discharge (ESD) with Microenvironment Dominated by Electrode Approaching Velocity Based on Wavelet Neural Network
<p id="p1">The microenvironment dominated by the electrode approaching velocity can be defined as the micro-gap ESD model. Micro-gap ESD is a time-domain continuous process, and the electrode approach velocity critically influences the characteristic discharge parameters. Based on two sub-processes,...
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| Autores principales: | , , , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1197175 |
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| Sumario: | <p id="p1">The microenvironment dominated by the electrode approaching velocity can be defined as the micro-gap ESD model. Micro-gap ESD is a time-domain continuous process, and the electrode approach velocity critically influences the characteristic discharge parameters. Based on two sub-processes, field electron emission and Townsend gas discharge, the experimental characteristic parameters are analyzed in time frequency. Wavelet neural network algorithm is also used to analyze and predict the discharge results. Remarkable variation appeared in discharge parameters of discharge current peak values, current derivative peak values, and arc length due to electrode approach velocity change in experimental measurement results of micro-gap electrostatic discharge. The higher the velocity, the larger the variation of discharge parameters. Threshold phenomenon emerged for discharge parameters variation with charge voltage due to electrode moving to the target at various velocities. For micro-gap ranging in 80–300 micrometers, electrode moving velocity enhanced the sub-process of electron surface emission. The approaching velocity is an important input index, and the wavelet neural network algorithm is used to analyze and predict the output results. The characteristics measured in the real experiment were compared with the prediction parameters from the wavelet neural network calculation result. According to the prediction data, a discussion was conducted on correctness accuracy and the discharge process trend analysis. Nonlinearity and discreteness are the main characteristics of discharge parameters.</p> |
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| ISBN: | 978-1-83769-476-1 |
| DOI: | 10.5772/intechopen.1008561 |