Abstract
Failure detection and recovery is one of the important operations of the health monitoring management system which plays a relevant role for keeping the reliability and availability of the failed sensor over an on-orbit system whole lifetime mission especially where the maintenance may be impossible. In this paper we have implemented Grey Wolf Optimization (GWO) for optimizing Support Vector Machines (SVM) in terms of recovering the detected failure of the failed sensor. The performance of the proposed model with GWO is compared against to four swarm algorithms; Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA), Moth Flame Optimizer (MFO) and Whale Optimizer Algorithm (WOA), four different evaluation aspects are used in this comparison; failure recovering accuracy, stability, convergence and computational time. The experiment is implemented using cube satellite telemetry data, the experimental results demonstrate that the optimization of SVM using GWO (SVM-GWO) model can be regarded as a promising success for satellite failure detection and recovery.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998)
Chen, J., Licheng, J.: Classification mechanism of support vector machines. In: Proceedings of 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, Beijing, China. IEEE (2000)
Qiang, W., Xuan, D.: Analysis of support vector machine classification. Comput. Anal. Appl. 8(2), 99–119 (2006)
Elhariri, E., El-Bendary, N., Mostafa, M., Fouad, M., Platos, J., Hassanien, A.E., Hussein, M.M.: Multi-class SVM based classification approach for tomato ripeness. In: Proceedings of 4th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA, Ostrava, Czech Republic (2013)
Zai, W.Y., Guo, W.X.: The fault detection and diagnosis for fractionating tower based on correlation coefficient. In: Proceedings of International Symposium on Computer, Consumer and Control, China, pp. 268–274. IEEE Computer Society (2016)
Kamalesh, S., Ganesh Kumar, P.: Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery. J. Exp. Theor. Artif. Intell. 29, 1362–3079 (2016)
Mirjalili, S., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Rezaei, H., Bozorg-Haddad, O., Chu, X.: Grey Wolf Optimization (GWO) algorithm. In: Bozorg-Haddad, O. (ed.) Advanced Optimization by Nature-Inspired Algorithms. Studies in Computational Intelligence, Karaj, Iran, vol. 720, pp. 81–91. Springer (2018). https://link.springer.com/content/pdf/10.1007%2F978-981-10-5221-7_9.pdf. Accessed 1 Apr 2018
Elhariri, E., El-Bendary, N., Hassanien, A.E., Abraham, A.: Grey wolf optimization for one-against-one multi-class support vector machines. In: Proceedings of Soft Computing and Pattern Recognition (SoCPaR), Fukuoka, Japan, pp. 7–12. IEEE (2015)
FunCube Real Time Data. http://warehouse.funcube.org.uk/. Accessed 1 Nov 2017
Jifri, M.H., Hassan, E.E., Miswan, N.H.: Forecasting performance of time series and regression in modeling electricity load demand. In: Proceedings of 7th IEEE International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia. IEEE (2017)
Bonyadi, M.R., Michalewicz, Z.: Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 20(3), 370–385 (2016)
Acknowledgement
This work is supported by Egypt Knowledge and Technology Alliance (E-KTA), which is funded by The Academy of Scientific Research & Technology (ASRT), and coordinated by National Authority for Remote Sensing & Space Sciences (NARSS).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdelghafar, S., Darwish, A., Hassanien, A.E. (2019). Cube Satellite Failure Detection and Recovery Using Optimized Support Vector Machine. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-99010-1_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99009-5
Online ISBN: 978-3-319-99010-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)