Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis


Article Preview

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved.



Materials Science Forum (Volumes 532-533)

Edited by:

Chengyu Jiang, Geng Liu, Dinghua Zhang and Xipeng Xu




W. S. Hao et al., "Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis", Materials Science Forum, Vols. 532-533, pp. 496-499, 2006

Online since:

December 2006




[1] V.N. Vapnik: Statistical Learning Theory (Wiley, New York, 1998).

[2] C.C. Chang and C.J. Lin: Neural Comput., Vol. 13 (2001), pp.2119-2147.

[3] R. Volker: Lecture Notes in Computer Science, Vol. 2191 (2001), p.246.

[4] M. Patricia, F. Cristina and C. Oscar: International Journal of Intelligent Systems, Vol. 20(2) (2005): pp.275-291.

[5] T. Hastie and R. Tibshirani: Advances in Neural Information Processing Systems (The MIT Press, 1998).

[6] M. Sugeno: Amsterdam, (1977), pp.89-102.