Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

Zhong Hai Li; Hao Fei Mao; Jian Guo Cui; Yan Zhang

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 55-57Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 55-57)

Pages:

747-752

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.55-57.747

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Online since:

May 2011

Authors:

Zhong Hai Li, Hao Fei Mao, Jian Guo Cui, Yan Zhang

Keywords:

Fault Diagnosis, LSSVM, Monitor, MSICA, Permanent Magnet Synchronous Motor (PMSM)

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