doi: 10.4304/jsw.6.10.1993-2000
Kernel Local Fuzzy Clustering Margin Fisher Discriminant Method Faced on Fault Diagnosis
- 1 Engineering Research Center of Advanced Mine Equipment Ministry of Education, Hunan University of Science and Technology Xiangtan, China
- 2 Hunan 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.
Keywords
- fuzzy clustering
- local margin
- fisher d
How to Cite
Guangbin Wang, Xuejun Li, and 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. https://doi.org/ 10.4304/jsw.6.10.1993-2000
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).