7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Real-time Diagnosis of Plasma Molecular Temperature Based on Visible ImageRegression Analysis
Shuai Yang1, Huan Chen1, Yunxuan Wang1, Chuan Li1, Yong Yang1
1
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong
University of Science and Technology
e-mail (speaker): 840085791@qq.com

The temperature parameter diagnosis of low-temperatureplasma plays an important role in the study of its
mechanism and applications in various fields. For
example, efficient and accurate diagnosis of plasma
molecular rotational and vibrational temperatures iscrucial in numerous domains, such as plasma medical
treatment, plasma spraying, and aerospace combustion
technology.[1] As the most commonly used method ofplasma temperature diagnosis, optical emission
spectroscopy (OES) requires the collection of emission
spectrums and manually fit them offline to obtain therotational and vibrational temperatures of the plasma.[2]Consequently, the need for real-time and accurate
parameter diagnosis has emerged as a focal point incontemporary plasma research. The rapid developmentof machine learning in recent years have provided new
ideas to address this challenge.
In the previous research of our team, we studied andanalyzed the temperature parameter characteristics andinternal mechanism of gas discharge plasma under
different experimental conditions.[3] In order to avoid theproblem of low efficiency of manual comparison andfitting of spectral data, we employed the Elastic-net
linear regression technology to analyze the plasma
emission spectrum and achieved ideal performance.[4]Moreover, to avoid costly spectral acquisition equipment,
we explored the use of visible image recognition
technology to diagnose plasma molecular temperatures,using convolutional neural network (CNN) to predict thetemperatures of five kinds of plasma discharge visible
images.[5] However, this approach essentially treated theproblem as an image classification task, and the small
number of classes makes the model generalization ability
very poor, and the predicted temperature range is limited
and discretized.
To address these limitations, we expanded upon ourprevious research by establishing a wide temperature
range visible image dataset for plasma discharge, andused deep learning image regression technology to train
and analyze the data. A plasma molecular temperatureintelligent diagnosis model based on visible image
regression analysis was established, achieving
continuous prediction of the rotational and vibrationaltemperatures of the plasma visible images. The specificexperimental process is illustrated in Figure 1 andinvolves the following steps:
1) The emission spectrum data of needle plate gas
discharge under various working conditions wascollected and machine learning linear regression
technology was utilized to quickly predict its rotationaland vibrational temperatures to avoid the problem of low
efficiency of manual comparison and fitting.

2) While collecting emission spectrum data, plasma
visible images under the same working conditions werecollected. A dataset was established based on themapping relationship between visible images andmolecular temperatures.
3) The chromaticity information in the plasma visible
image or CNN image regression automatic feature
extraction was used to perform regression analysis on its
rotational temperature and vibrational temperature, so as
to realize the continuous prediction and diagnosis of themolecular temperatures of the plasma visible images.
In conclusion, our research presents a novel approachto real-time diagnosis of plasma molecular temperaturesusing visible image regression analysis. This method
offers a more efficient and accurate alternative to
traditional techniques, paving the way for advancements
in plasma research and its applications across variousindustries.
This work is supported by National Natural Science
Foundation of China (Grant No. 52207158).
References[1] M. Laroussi et al, J. Appl. Phys. 122, 020901 (2017)[2] D. Staack et al, Plasma Sources Sci. Technol. 15,818-827 (2006)[3] S. Yang et al, CIEEC. “Study on Temperature
Parameter Characteristics of Needle-Plate Air Discharge
Plasma” (2022)[4] Y. Yang et al, IEEE Trans. Plasma Sci. 51, 466-473(2023)[5] Z. Yu et al, CIEEC. “Plasma Real-time Diagnostics
Based on Visual Image Recognition” (2021)

Figure 1. Specific experimental process for real-time
diagnosis of plasma molecular temperatures based onvisible image chromaticity analysis and CNN feature
extraction regression analysis.