Stochastic modeling of air electrostatic discharge parameters

Yang, Xiu; Sagan, Samuel; Battini, Advika; Ma, Xiao; Raginsky, Maxim; Rosenbaum, Elyse

doi:10.1109/irps.2018.8353548

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Moreover, the different orientation of the same ESD generator may lead to different test results. It must be mentioned that during the verification of the ESD generators, ESD discharges are contact discharges because the reproducibility of air discharge currents is a major problem [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], NBA was used to predict the rise time and the maximum current using as input data humidity and the voltage before discharge. In the current study, NBA is used as a prediction tool for EM fields produced by ESD generators from the discharge current's characteristics in terms of the distance and the direction of the ESD generator from the tested equipment.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques for the Prediction of the Magnetic and Electric Field of Electrostatic Discharges

2022

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The magnetic and electric fields of electrostatic discharges are assessed using the Naïve Bayes algorithm, a machine learning technique. Laboratory data from electrostatic discharge generators were used for the implementation of this algorithm. The applied machine learning algorithm can be used to predict the radiated field knowing the discharge current. The results of the Naïve Bayes algorithm are compared to a previous software tool derived by Artificial Neural Networks, proving its better outcome. The Naïve Bayes algorithm has excellent performance on most classification tasks, despite its simplicity, and usually is more accurate than many sophisticated methods. The proposed algorithm can be used by laboratories that conduct electrostatic discharge tests on electronic equipment. It will be a useful software tool, since they will be able to predict the radiating electromagnetic field by simply measuring the discharge current from the electrostatic discharge generators.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for the Prediction of the Magnetic and Electric Field of Electrostatic Discharges

2022

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“…This Standard prescribes the calibration procedure of the ESD generators (or guns) that simulate the real ESD current, which must be made only for contact discharges. This happens due to the high reproducibility of contact discharges in addition to air discharges, in which the peak current may differ from one discharge to another due to the different arcs that are produced [25][26][27][28][29]. It is also the simplest case to examine in an uncertainty evaluation budget.…”

Section: Introductionmentioning

confidence: 99%

Rise Time and Peak Current Measurement of ESD Current from Air Discharges with Uncertainty Calculation

2022

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The greater number of electrostatic discharges (ESDs) that occur in nature is by air rather than by contact. However, due to low reproducibility in the current of air discharges, the IEC 61000-4-2 defines that the current’s calibration of an ESD gun must be made only for contact discharges. In the work presented here, there is an attempt to improve the poor reproducibility of air discharges by a significant observation derived from ESD measurements and, more specifically, the relationship between the rise time and the peak current for every ESD gun. This fact validated in this paper from current measurements of two different ESD guns will help all who are involved in ESD measurements in EMC laboratories by reducing the existing uncertainty in measurements of air discharges.

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