DocumentCode :
3501751
Title :
Data mining-based fault detection and prediction methods for in-orbit satellite
Author :
Tianshe Yang ; Bin Chen ; Yu Gao ; Junhua Feng ; Hailong Zhang ; Xiaole Wang
Author_Institution :
State Key Lab. of Astronaut. Dynamics, Xi´an Satellite Control Center, Xi´an, China
Volume :
02
fYear :
2013
fDate :
16-18 Aug. 2013
Firstpage :
805
Lastpage :
808
Abstract :
Fault detection and prediction is one of the key technologies for health monitoring of in-orbit satellites. The fault detection and prediction methods for in-orbit satellites is studied by the data mining technology. The deep mining method for telemetry of the in-orbit satellite is studied. Then, several fault detection and prediction methods are proposed, which have been developed and used in fault detection and prediction of actual in-orbit satellites. The results show that the proposed methods are effective and feasible.
Keywords :
data mining; fault diagnosis; telemetry; data mining-based fault detection and prediction methods; deep mining method; health monitoring; in-orbit satellite; satellite telemetry data; Estimation theory; Satellite broadcasting; Satellites; Telemetry; data mining; fault detection; fault prediction; in-orbit satellite; telemetry data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measurement, Information and Control (ICMIC), 2013 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-1390-9
Type :
conf
DOI :
10.1109/MIC.2013.6758085
Filename :
6758085
Link To Document :
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