Paper Title:
Fault Diagnosis Method Based on Supervised Manifold Learning and SVM
  Abstract

In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.

  Info
Periodical
Edited by
Yuhang Yang, Xilong Qu, Yiping Luo and Aimin Yang
Pages
223-227
DOI
10.4028/www.scientific.net/AMR.216.223
Citation
G. B. Wang, X. J. Li, Z. C. He, Y.Q. Kong, "Fault Diagnosis Method Based on Supervised Manifold Learning and SVM", Advanced Materials Research, Vol. 216, pp. 223-227, 2011
Online since
March 2011
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Price
$32.00
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