Recognition and Judgment of Electromagnetic Disturbance of Telemetry Equipment Based on Machine Learning

DOI

10.2991/icmcm-16.2016.83How to use a DOI?

Keywords

Support Vector Machine; Telemetry equipment; State identification.

Abstract

The complex electromagnetic environment will interfere with the performance of telemetry equipment, and the interference can not be directly observed and identified. The state of the data mining equipment is subject to the problem of electromagnetic interference, the use of support vector machine method for the equipment subject to interference and not subject to interference in a variety of state information feature extraction, training machine learning model, using the training model on the current Equipment status to test, to determine its interference situation. Based on the above methods, this paper designs and implements a machine learning-based interference monitoring and analysis program. The method is tested with the state data of the servo and baseband memory. The experimental results show that the method can detect the disturbance of the equipment and can be used to monitor the health of the system in real time.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Luolan Yang
AU  - Jiaqi Sun
AU  - Linqiao Jia
PY  - 2016/12
DA  - 2016/12
TI  - Recognition and Judgment of Electromagnetic Disturbance of Telemetry Equipment Based on Machine Learning
BT  - Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)
PB  - Atlantis Press
SP  - 424
EP  - 431
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmcm-16.2016.83
DO  - 10.2991/icmcm-16.2016.83
ID  - Yang2016/12
ER  -