SPARK(编程语言)
信号(编程语言)
火花放电
声学
声发射
计算机科学
材料科学
电气工程
工程类
物理
电压
程序设计语言
作者
Jun 俊 XIONG 熊,Shiyu 诗宇 LU 卢,Xiaoming 晓明 LIU 刘,Wenjun 文俊 ZHOU 周,Xiaoming 晓明 ZHA 查,Xuekai 学凯 PEI 裴
出处
期刊:Plasma Science & Technology
[IOP Publishing]
日期:2024-08-01
卷期号:26 (8): 085403-085403
标识
DOI:10.1088/2058-6272/ad495e
摘要
Abstract Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k -Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k -Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE = 0.00571) and the highest -squared value ( ). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.
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