Citation: Wang, J.; Ji, W.; Du, Q.;
Xing, Z.; Xie, X.; Zhang, Q. A Long
Short-Term Memory Network for
Plasma Diagnosis from Langmuir
Probe Data. Sensors 2022, 22, 4281.
https://doi.org/10.3390/
s22114281
Academic Editor: Hossam A. Gabbar
Received: 7 April 2022
Accepted: 1 June 2022
Published: 4 June 2022
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Article
A Long Short-Term Memory Network for Plasma Diagnosis
from Langmuir Probe Data
Jin Wang
1,2
, Wenzhu Ji
2
, Qingfu Du
2
, Zanyang Xing
1
, Xinyao Xie
1
and Qinghe Zhang
1,
*
1
Institute of Space Sciences, Shandong University, Weihai 264209, China; jinwang@mail.sdu.edu.cn (J.W.);
xingzanyang@sdu.edu.cn (Z.X.); messi@mail.sdu.edu.cn (X.X.)
2
School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China;
202037483@mail.sdu.edu.cn (W.J.); dqf@sdu.edu.cn (Q.D.)
* Correspondence: zhangqinghe@sdu.edu.cn
Abstract:
Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the
traditional diagnosis theory is affected by many factors, and it is difficult to obtain accurate diagnosis
results. In this study, a long short-term memory (LSTM) approach is used for plasma probe diagnosis
to derive electron density (N
e
) and temperature (T
e
) more accurately and quickly. The LSTM network
uses the data collected by Langmuir probes as input to eliminate the influence of the discharge
device on the diagnosis that can be applied to a variety of discharge environments and even space
ionospheric diagnosis. In the high-vacuum gas discharge environment, the Langmuir probe is used to
obtain current–voltage (I–V) characteristic curves under different N
e
and T
e
. A part of the data input
network is selected for training, the other part of the data is used as the test set to test the network,
and the parameters are adjusted to make the network obtain better prediction results. Two indexes,
namely, mean squared error (MSE) and mean absolute percentage error (MAPE), are evaluated to
calculate the prediction accuracy. The results show that using LSTM to diagnose plasma can reduce
the impact of probe surface contamination on the traditional diagnosis methods and can accurately
diagnose the underdense plasma. In addition, compared with T
e
, the N
e
diagnosis result output by
LSTM is more accurate.
Keywords: LSTM; machine learning; Langmuir probe; plasma diagnosis
1. Introduction
Plasma is a complex thermodynamic system composed of electrons, ions, and neutral
particles, which widely exists in cosmic space. Plasma is a conductive fluid as a whole,
showing electrical neutrality macroscopically, but under the action of an electromagnetic
field, energy transmission can occur. The measurement of the plasma state has always
been the focus of researchers. The state of plasma can be characterized by electron density
(N
e
), electron temperature (T
e
), plasma space potential (V
p
), and other parameters, among
which the most crucial are N
e
and T
e
. N
e
describes the number of electrons per unit volume,
while T
e
describes the kinetic energy possessed by electrons. Under thermal equilibrium
conditions, T
e
is equal to the ion temperature (T
i
). Most diagnostic methods for plasma are
aimed at obtaining N
e
and T
e
. The diagnosis methods of plasma are divided into telemetry
diagnosis and in situ diagnosis. Telemetry diagnosis includes microwave diagnosis and
spectral diagnosis. Langmuir probe diagnosis is the most common in situ diagnosis
technology, which has been widely used in laboratory and space plasma detection [
1
–
7
].
Compared with telemetry diagnosis, the Langmuir probe can obtain more reliable and
accurate diagnosis results.
However, the traditional diagnostic method of Langmuir probes highly depends on
the acquisition of the current–voltage (I–V) characteristic curve. The degree of deviation of
the collected I–V characteristic curve from the actual value directly affects the reliability
Sensors 2022, 22, 4281. https://doi.org/10.3390/s22114281 https://www.mdpi.com/journal/sensors
