Research on Fault Diagnosis of Weapon Equipment Based on Support Vector Machines

Lin Zhang; Tao Liu; An Tang Zhang; Peng Xu; Ke Lian

doi:10.4028/www.scientific.net/AMR.466-467.1242

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467Research on Fault Diagnosis of Weapon Equipment...

Research on Fault Diagnosis of Weapon Equipment Based on Support Vector Machines

Abstract:

Surface-to-air missile equipment is an advanced aerial defense weapon equipment of middle-high altitude intermediate range in our army, and this weapon equipment is also shouldering the significant task of antiaircraft defense of our country. Therefore, researching on its Fault Intelligent Diagnosis System has an important practical significance and military value on improving the weapon equipment’s renewing of fault and keeping the army’s battle effectiveness.

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Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

1242-1245

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.1242

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Online since:

February 2012

Authors:

Lin Zhang, Tao Liu, An Tang Zhang, Peng Xu, Ke Lian

Keywords:

Fault Diagnosis, Support Vector Machine (SVM)

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References

[1] Wang Zhong-sheng. Intelligent Fault Diagnosis and Fault Tolerant Control [M]. North West University of Technology Press, 2005.

Google Scholar

[2] Weston J, Watkins C. Multi-class supports vector machines[R]. Technical Report CSD-TR-98-04, Dept. of Computer Science, and University of London, 1998:1～10.

Google Scholar

[3] Chih-Wei Hsu, Chih-Jen Lin. A comparison of methods for multi-class support vector machines[A].IEEE Transactions on Neural Networks,2002,13(2):415～425.

DOI: 10.1109/72.991427

Google Scholar

[4] Vapnik V N.Statistical learning theory [M]. John Viley & Sons, Inc, New York, 1998.

Google Scholar

[5] Krssel U. Pairwise classification and support vector machines [J]. In Scholkopf B.et al (Eds.),Advances in kernel Methods-Support vector learning, Cambridge, MA, MIT Press, 1999.

DOI: 10.7551/mitpress/1130.003.0020

Google Scholar