Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

Abstract:

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The paper presents a motor bearing fault diagnosis method based on MSICA (Multi-scale Independent Principal Component Analysis) and LSSVM (Least Squares Support Vector Machine). MSICA is introduced into motor fault diagnosis. First, wavelet decomposition is used, and then ICA models are built by wavelet coefficients in each scale, which detect fault, and finally LSSVM is used to classify fault type. Conclusions are obtained from the analysis of the experimental data provided by Case Western Reserve University’s Bearing Data Website. And it indicates that the proposed method is simple and effective.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

747-752

DOI:

10.4028/www.scientific.net/AMM.55-57.747

Citation:

Z. H. Li et al., "Motor Bearing Fault Diagnosis Based on MSICA-LSSVM", Applied Mechanics and Materials, Vols. 55-57, pp. 747-752, 2011

Online since:

May 2011

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Price:

$35.00

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