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The Scientific World Journal
Volume 2014 (2014), Article ID 582042, 11 pages
Research Article

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

1College of Computer, National University of Defense Technology, Changsha 410073, China
2Xiangyang School for NCOs, Xiangyang 441118, China

Received 6 May 2014; Accepted 16 July 2014; Published 12 August 2014

Academic Editor: K. I. Ramachandran

Copyright © 2014 Hong Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.