Time-Frequency Analysis of Electrostatic Discharge Signal based on Wavelet Transform


Electrostatic discharge signal is a non-stationary signal whose frequency is varied with time. Time-frequency analysis is able to reveal the more useful information hidden in the ESD signal. In this letter, we propose a time-frequency analysis approach using the wavelet transform. Based on the Morlet wavelet, this paper analyzes the actual ESD signal and obtains its time-frequency characteristic. 2-D and 3-D ESD data are showed in this paper. The result shown that high frequency component of ESD can reach 0.6GHz. In addition, the energy of the measured signal is mainly concentrated in the range of 100 to 200 MHz. The high-frequency component attenuations rapidly and the low-frequency duration is relatively long. It can provide some new idea for extraction or signal denoising.

Meeting Name

2018 12th International Conference on Anti-Counterfeiting, Security and Identification, ASID (2018: Nov. 9-11, Xiamen, China)


Electrical and Computer Engineering


This work was supported by Guizhou Province Project of Innovation Talents Teams of Electrostatic and Electromagnetic Protection (No.[2016]5653) , by Academician Liu Shanghe Fund of Electrostatic Protection Research(Grant No.BOIMTLSHJD20161004), by 2016 Central Government Special Fund Supporting Guizhou Development of Science and Technology (No.QKZYD[2016]4006), by Guizhou Province Project of Innovation Talent Teams of Saving Energy and Controlling of Engineering Machines (No.[2014]4013).

Keywords and Phrases

Electrostatic devices; Electrostatic discharge; Electrostatics; High frequency components; Low-frequency; Measured signals; Morlet Wavelet; Nonstationary signals; Time frequency analysis; Time frequency characteristics; Time-frequency analysis approaches; Wavelet transforms; Time-frequency analysis

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

2163-5056; 2163-5048

Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 IEEE Computer Society, All rights reserved.

Publication Date

01 Nov 2018