声学
短时傅里叶变换
信号(编程语言)
声发射
支持向量机
快速傅里叶变换
辉光放电
材料科学
计算机科学
电晕放电
傅里叶变换
物理
人工智能
傅里叶分析
电极
等离子体
算法
量子力学
程序设计语言
作者
Zilan Xiong,Yuqi Wang,Mengqi Li
出处
期刊:Physica Scripta
[IOP Publishing]
日期:2022-12-26
卷期号:98 (1): 015613-015613
标识
DOI:10.1088/1402-4896/acab98
摘要
Abstract Gas discharge will produce rich electromagnetic, optical as well as acoustic signals. Compared with the other signals, acoustic signals are also significant and would offer non-contact, low cost and easy-operation approach for online discharging monitoring, which require more attention and intensive study. In this paper, we studied the characteristics of acoustic signals in the corona, transient glow, spark, and glow discharging modes generated in a DC pin-to-pin configuration and developed a method using acoustic signals to classify the different discharge modes. The acoustic signals of the discharge at different gaps were recorded by adjusting the gap distance. 250 sets of acoustic signal samples were collected for each discharging mode. It was found that acoustic signals behave differently in different modes. Based on the short-time Fourier transform (STFT) of the acoustic signals, a novel method for discharge mode classification using the support vector machine (SVM) approach was developed. The final predictive accuracy of the trained classifier exceeds 90%.
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