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Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis | IEEE Journals & Magazine | IEEE Xplore

Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis


Abstract:

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing...Show More

Abstract:

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 61, Issue: 5, May 2014)
Page(s): 2441 - 2451
Date of Publication: 21 August 2013

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