Paper Title:
Fault Diagnosis of Rolling Bearings Based on LLE_KFDA
  Abstract

Locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimension reduction. In this paper,LLE manifold learning algorithm is introduced into the field of equipment fault diagnosis firstly, a method of the fault diagnosis based on LLE_KFDA is proposed. By LLE algorithm, original sample data is directly mapped to its’ intrinsical dimension space,which data still keep primary nonlinear form. then via kernel fisher discriminant analysis(KFDA), the characteristics data in intrinsical dimension space are mapped into knernel high-dimensional linear space,and then different fault data are discriminated based on a criterion of between-class and insid-class deviatione ratio maximum. LLE_KFDA algorithm is based on original data, avoided from fall of pattern recognition ability which caused by inappropriate or blind choice of the feature parameters in the traditional fault diagnosis method.The experiment to fault diagnosis of rolling bearing shows this method can effectively identify the equipment fault pattern, diagnostic result is good.

  Info
Periodical
Materials Science Forum (Volumes 626-627)
Edited by
Dongming Guo, Jun Wang, Zhenyuan Jia, Renke Kang, Hang Gao, and Xuyue Wang
Pages
529-534
DOI
10.4028/www.scientific.net/MSF.626-627.529
Citation
G. B. Wang, Y.I. Liu, X.Q. Zhao, "Fault Diagnosis of Rolling Bearings Based on LLE_KFDA", Materials Science Forum, Vols. 626-627, pp. 529-534, 2009
Online since
August 2009
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$32.00
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