Volume 6 Number 10 (Oct. 2011)
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JSW 2011 Vol.6(10): 1993-2000 ISSN: 1796-217X
doi: 10.4304/jsw.6.10.1993-2000

Kernel Local Fuzzy Clustering Margin Fisher Discriminant Method Faced on Fault Diagnosis

Guangbin Wang1, Xuejun Li2, Kuang Fang He2

1Engineering Research Center of Advanced Mine Equipment Ministry of Education, Hunan University of Science and Technology Xiangtan, China
2Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan, China

Abstract—In order to better identify the fault of rotor system,one new method based on local fuzzy clustering margin fisher discriminant (LFCMFD) was proposed. For each point on manifold, the farthest point in local neighborhood and the nearest point outside local neighborhood usually constituted the local margin. LFCMFD introduced fuzzy cluster analysis algorithm, eliminated the influence of pseudo-margin points, obtained real local margin, compute with-class scatter and between-class scatte, established local magin fisher discriminant function, found optimal fault diagnosis vector,and then identified the fault class of new testing data by this vector. In order to improve the nonlinear analysis ability of LFCMFD, considering kernel mapping idea, training data with supervision information were mapped to kernel space, constructed kernel fisher discriminant function, LFCMFD algorithm based on kernel method (KLFCMFD)was proposed. The experiment showed, KLFCMFD algorithm had best effect in comparison to other manifold learning algorithm to the rotor fault diagnosis,and fully identify fault class when selecting the appropriate parameters.

Index Terms—fuzzy clustering, local margin, fisher discriminant, kernel mapping, fault diagnosis


Cite: Guangbin Wang, Xuejun Li, Kuang Fang He, "Kernel Local Fuzzy Clustering Margin Fisher Discriminant Method Faced on Fault Diagnosis," Journal of Software vol. 6, no. 10, pp. 1993-2000, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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