Abstract
In daily life fault diagnosis is widely used production. With the rapid development of science and technology, the new high-tech products emerged. It is not enough data of samples. Conventional approach is ineffective. It is need to find a good method. The least squares support vector machine algorithm and the proximal of support vector machine applied to fault diagnosis. Through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even. On fault diagnosis the training speed has been to improve and the cost of building has been reduced. Improve overall system performance of fault diagnosis.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vapnik, V.N.: Statistical learning theory. Springer, New York (1995)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20(3), 273–297 (1995)
Deng, n., et al.: Support vector Machine Theory, algorithms and Development, p. 176. Science Press, Beijing (2009) (in Chinese)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. J. Neural Processing Letters, 293 (1999)
Osuna, E., et al.: An improved training algorithm for support vector machines. In: J. Nerual Networks for Signal Processing VII-Proceedings of the 1997 IEEE Workshop, p. 276. IEEE (1997)
Fung, G., Mangasarian, O.L.: Proximal support vector machine classifiers. In: Proceedings of International Conference of Knowledge Discovery and Data Mining, pp. 77–86 (2010)
Platt, J.: Fast training of support Vector machines using sequential optimization. In: Dietterich, T.G. (ed.) Advances in Kernel Methods Support Vector Learning, p. 233. MIT Press (1996)
Dietterich, T.G., et al.: Solving multi-class learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Xu, J., et al.: Nonlinear process monitoring and fault diagnosis based on KPCA and MKL-SVM. Chinese Journal of Scientific Instrument (11), 2428–2433 (2010) (in Chinese)
Chen, W., et al.: Application of Fault Diagnosis for A Equipment Based on Binary Tree Support Vector Machines. Avionics Technology 41(2), 19–23 (2010)
Fang, K., et al.: Insulation fault diagnosis for traction transformer based on PSO and SVM. Computer Aided Engineering 19(3), 83–86 (2010) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Zhu, Y., Lin, S., Liu, X. (2011). Application of Least Squares Support Vector Machine in Fault Diagnosis. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-27452-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27451-0
Online ISBN: 978-3-642-27452-7
eBook Packages: Computer ScienceComputer Science (R0)