Abstract
Fault prediction is the core content and crucial technology for health monitoring of the in-orbit spacecraft, and predicting the future trend of telemetry data is the prerequisite and basis for fault prediction. This paper presents a state trend prediction method for spacecraft based on particle swarm optimization (PSO) and support vector regression (SVR). The method applies SVR to construct a regression prediction model of telemetry data. SVR is a learning procedure based on statistical learning theory, which employs the training data to build an excellent prediction model in the situations of small sample. The complexity and generalization performance of the SVR model is influenced by its training parameters. In this paper, PSO is applied to optimize the parameters of SVR model. The results show that the method is efficient and practical for telemetry data prediction of the in-orbit spacecraft.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang YL et al (2013) Research on method of electronic equipment fault prediction. In: Proceedings of IEEE fourth international conference on digital manufacturing and automation (ICDMA), pp 1080–1085
Yang TS, Chen B, Gao Y et al (2013) Data mining-based fault detection and prediction methods for in-orbit satellite. In: Proceedings of IEEE international conference on measurement, information and control (ICMIC), pp 805–808
Gao Y, Yang TS, Xu MQ et al (2012) A neural network approach for satellite telemetry data prediction. In: Proceedings of IEEE international conference on electronics, communications and control (ICECC), pp 150–153
Yang TS, Chen B, Zhang HL et al (2013) State trend prediction of spacecraft based on BP neural network. In: Proceedings of IEEE international conference on measurement, information and control (ICMIC), pp 809–812
Cui JG, Zhang L, Wang GH et al (2014) Fault prediction method of the marine gas turbine based on neural network-Markov. Appl Mech Mater 538:171–174
Deng S, Jing B, Zhou HL et al (2012) Fault prediction method based on improved AdaBoost-SVR algorithm. Acta Armament 33(8):991–996
Fan J, Tang Y (2013) An EMD-SVR method for non-stationary time series prediction. In: Proceedings of IEEE international conference on quality, reliability, risk, maintenance, and safety engineering (QR2MSE), pp 1765–1770
Liu L, Shen J, Zhao H (2012) Fault forecast of electronic equipment based on ε-SVR. Web information systems and mining. Springer, Berlin, pp 521–527
Poyhonen S, Arkkio A, Jover P et al (2005) Coupling pairwise support vector machines for fault classification. Control Eng Pract 13(6):759–769
Lin CJ. LIBSVM. http://www.csie.ntu.edu.tw/~cjlin/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, Y., Yang, T., Li, W., Zhang, H. (2015). State Trend Prediction of Spacecraft Using PSO-SVR. In: Shen, R., Qian, W. (eds) Proceedings of the 27th Conference of Spacecraft TT&C Technology in China. Lecture Notes in Electrical Engineering, vol 323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44687-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-662-44687-4_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44686-7
Online ISBN: 978-3-662-44687-4
eBook Packages: EngineeringEngineering (R0)