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Jun Xiong, Shiyu Lu, Xiaoming Liu, Wenjun Zhou, Xiaoming Zha, Xuekai Pei. Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal[J]. Plasma Science and Technology. DOI: 10.1088/2058-6272/ad495e
Citation: Jun Xiong, Shiyu Lu, Xiaoming Liu, Wenjun Zhou, Xiaoming Zha, Xuekai Pei. Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal[J]. Plasma Science and Technology. DOI: 10.1088/2058-6272/ad495e

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

  • 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 R-squared value (R^2=0.93877). 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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