Fault Diagnosis Method Based on Supervised Manifold Learning and SVM

Abstract:

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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 et al., "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:

$35.00

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