Abstract
Diagnosis of reaction wheel faults is very significant to ensure long-term stable satellite attitude control system operation. Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem of small sampling, non-linearity and high dimensionality. However, it is difficult to select suitable parameters of SVM. Particle Swarm Optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking. The optimization method not only has strong global search capability, but is also very simple to apply. However, PSO algorithms are still not mature enough for handling some of the more complicated problems as the one posed by SVM. Therefore an improved PSO algorithm is proposed and applied in parameter optimization of support vector machine as IPSO-SVM. The characteristics of satellite dynamic control process include three typical reaction wheel failures. Here an IPSO-SVM is used in fault diagnosis and compared with neural network-based diagnostic methods. Simulation results show that the improved PSO can effectively avoid the premature phenomenon; it can also optimize the SVM parameters, and achieve higher diagnostic accuracy than artificial neural network-based diagnostic methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tafazoli, M.: A study of on-orbit spacecraft failures. Acta Astronautica 64, 195–205 (2009)
Geng, L.-H., Xiao, D.-Y., Wang, Q., et al.: Attitude control model identification of on-orbit satellites actuated by reaction wheels. Acta Astronautica 66, 714–721 (2010)
Barua, A., Sinha, P., Khorasani, K., et al.: A novel fault tree approach for identifying potentiall causes of satellite reaction wheel failure. In: Proceedings of the 2005 IEEE Conference on Control applications Toronto, Canada (August 2005)
Li, Z.Q., Ma, L., Khorasani, K.: A dynamic neural network based reaction wheel fault diagnosis for satellites. In: 2006 International Joint Conference on Neural Networks, Sheraton Vancouver wall centre hotel, Vancouver, Canada (July 2006)
Zhou, J., Liu, Q., Jin, G., et al.: Reliability Modeling for Momentum Wheel based on Data Mining of failure-Physics. In: 2010 Third Internatinal Conference on Knowledge Discovery and Data Mining, pp. 115–118. IEEE, Los Alamitos (2010)
Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. of the 6th Int’l. Symp. on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Ali, M.M., Kaelo, P.: Improved particle swarm algorithms for global optimization. Applied mathematics and computation 196, 578–593 (2008)
Vapnik, V.N.: The Nature of statistical Learning Theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 3, 293–300 (1999)
Yuan, S.-F., Chu, F.-L.: Fault diagnositics based on particle swarm optimisation and support vector machines. Mechanical Systems and Signal Processing 21, 1787–1798 (2007)
Tang, X., Zhuang, L., Cai, J., et al.: Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowledge based systems (2010) (article in press)
Yu-hui, S., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. of Congress on Evolution Computation, pp. 1945–1950 (1999)
Zhao, H.-b., Yin, S.: Geomechanical parameters identification by particle swarm optimization and support vector machine. Applied mathematical modeling 33, 3997–4012 (2009)
Kressel, U.: Pairwise classification and support vector machines. In: Scholkopf, B., et al. (eds.) Advances in kernel Methods-Support vector learning, pp. 255–268. MIT Press, Cambridge (1999)
Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, D., Dong, Y., Sarosh, A. (2010). An Improved PSO-SVM Approach for Multi-faults Diagnosis of Satellite Reaction Wheel. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-16527-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16526-9
Online ISBN: 978-3-642-16527-6
eBook Packages: Computer ScienceComputer Science (R0)