The Fault Diagnosis Method of Support Vector Machine Based on Kernel Fuzzy C-Means Membership

Jin Chu Zheng; Yong Qin

doi:10.4028/www.scientific.net/AMR.1070-1072.2083

Paper Titles

Entropy and Distance Measure of Intuitionistic Fuzzy Soft Sets
p.2056

Moving Target Tracking Based on Trajectory Prediction
p.2062

Multi-Relational Naïve Bayesian Classification Based on the Selection of Relations
p.2066

Parameter Estimation of Weibull Distribution Model Based on Ant Colony Algorithm
p.2073

Vehicle Operating Fault Regularity and its Application in Inspection and Maintenance
p.2079

The Fault Diagnosis Method of Support Vector Machine Based on Kernel Fuzzy C-Means Membership
p.2083

Research on Conditioning and Application for the F²N²
p.2087

Knowledge-Based Defect Prevention Research for Electric Power Information System
p.2091

Typical Defects Sample Selection Methods Research for Electric Power Information System
p.2101

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1070-1072The Fault Diagnosis Method of Support Vector...

The Fault Diagnosis Method of Support Vector Machine Based on Kernel Fuzzy C-Means Membership

Abstract:

The rolling bearing is widely used in rail vehicles. The detection and diagnosis of the bearing is of great theoretical value and practical significance. The fault diagnosis method of support vector machine (SVM) based on kernel fuzzy C-means (KFCM) membership degrees can have very good results. Support vector machine based on statistical learning theory do well in terms of classification .This method clusters according to the sample membership degrees and puts data into nuclear space. It can highlight samples of differences in features. The training sample size has been greatly reduced, while training speed and the rate of correct classification is improved. The experiment results show that this method is feasible.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1070-1072)

Pages:

2083-2086

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.2083

Citation:

Cite this paper

Online since:

December 2014

Authors:

Jin Chu Zheng, Yong Qin*

Keywords:

Kernel Fuzzy C-Means Clustering, Rolling Bearing, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Michael Hutchinson. Railway rolling stock running fault monitoring and diagnosis technique research and application . Chinese railways, (2007).

Google Scholar

[2] Cohen L. Time-frequency distribution-a review. Proceedings of the IEEE, 1989, 77(7): pp.941-981.

Google Scholar

[3] Mallat S. A theory for multi-resolution decomposition, the wavelet representation. IEEE Trans. P. A. M. I, 1989, 11(7): 674-689.

Google Scholar

[4] A1-Badour F, Sunar M, Cheded L. Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing, 2011, 25(6): pp.2083-2101.

DOI: 10.1016/j.ymssp.2011.01.017

Google Scholar

[5] Yalin Kong. Research on Fault Diagnosis Method and Application for Rolling Element Bearing Based on Vibration Signal. Dalian University of Technology, (2005).

Google Scholar

[6] Cheng Jun-sheng, YANG Yu, De-jie. The local mean decomposition method and its application to gear fault diagnosis. Journal of Vibration Engineering, 2009, 22 (1): pp.76-84.

Google Scholar

[7] V.N. Vapink, An overview of statistical learning theory, IEEE Transactions on Neural Networks 10 (5) 1999, p.988–999.

Google Scholar