An Circuit Fault Diagnosis Method with K-Means Kernel Density Estimation

Jing Tang; Xian Jun Shi; Wen Guang Zhang

doi:10.4028/www.scientific.net/AMM.66-68.203

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 66-68An Circuit Fault Diagnosis Method with K-Means...

An Circuit Fault Diagnosis Method with K-Means Kernel Density Estimation

Abstract:

A K-Means kernel density estimation was proposed and it was used in the pretreatment process of circuit fault diagnosis. The unequal division and losing division problem caused by the traditional method are solved by this method. It also avoid the singular problem which is usually caused by the high dimension of characteristic data. A kernel function is designed and it was integrated with fuzzy support vector machine method to solve the classification problem of multi-faults . At last, a solution of optimal bandwidth is given to improve the proposed method.

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Periodical:

Applied Mechanics and Materials (Volumes 66-68)

Pages:

203-206

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.66-68.203

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Online since:

July 2011

Authors:

Jing Tang, Xian Jun Shi, Wen Guang Zhang

Keywords:

Analog Circuit, Fault Diagnosis, Fuzzy Support Vector Machine, Kernel Function, K-Mean

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References

[1] VAPNIK V N. Statistical learning theory [M]. New York: Springer, (1995).

Google Scholar

[2] Yuwang, Zhihua zhou, Machine Learning and its application, Beijing, Pressed by Tinghua University, 2006: 315.

Google Scholar

[3] Joh, n Shawe-Taylor, Nello Cristianini. The kernel method of mode analysis [M]. Beijing, Pressed by Machine industry, 2006: 246.

Google Scholar

[4] Bo Cheng, Hongwei Liu, An optimal kernel althorim for high discrimination radar distance figure, the Journal of electronics[J], 2006: 34(6): 1146-1151.

Google Scholar

[5] B. Chen, H. Liu, Z. Bao. A kernel optimization method based on the localized kernel Fisher criterion. Patterns Recognition [J], 2007: 41(3): 1098-1109.

DOI: 10.1016/j.patcog.2007.08.009

Google Scholar

[6] Daoqiang Zhang, Songcan Chen, Zhihua Zhou. Learning the kernel parameters in kernel minimum distance classifer. Pattern Recognition[J]. 06: 39(1): 133-135.

DOI: 10.1016/j.patcog.2005.08.001

Google Scholar