Research and Application of System Equipment Fault Diagnosis Based on Satellite Ground Station

• Published in: 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC) pp. 66 - 72
• Main Authors: Tian, Ke, Wang, Yuwen, He, Xiaotao, Qu, Xuanrui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2020
• Subjects: Adaptation models; Computational modeling; Control equipment; Data mining; Fault diagnosis; Feature extraction; Kernel Principal Component Analysis; Least Squares Support Vector Machine Algorithm; Particle Swarm Algorithm; Satellite ground station; Satellite ground stations
• DOI: 10.1109/ICCEIC51584.2020.00021

In order to solve the problems of low accuracy of the fault detection of the measurement and control equipment of the satellite ground station and unable to check the fault type in time, especially the potential risks in practical engineering applications, an overall structure of the fault diagnosis system was studied and designed. This paper elaborates the composition of the satellite ground station measurement and control equipment and the extraction of fault information, introduces the principle of the fault diagnosis of the measurement and control equipment, and designs the fault diagnosis model combined with this principle. Compared with traditional methods, this model brings in the methods of Kernel Principal Component Analysis, Least Squares Support Vector Machine Algorithm and Particle Swarm Algorithm. The results show that the application of the model reduces the number of data dimension and the running computation time and realizes the adaptive extraction of deep fault features. It not only promotes the diagnostic accuracy, automation and intelligence of the system, but also improves the safety, reliability and work efficiency of the system.