Motor bearing fault diagnosis using trace ratio linear discriminant analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number6583974
Pages (from-to)2441-2451
Journal / PublicationIEEE Transactions on Industrial Electronics
Volume61
Issue number5
Online published21 Aug 2013
Publication statusPublished - May 2014

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. © 2013 IEEE.

Research Area(s)

  • Bearing, fault diagnosis, linear discriminant analysis (LDA), pattern recognition, trace ratio (TR) criterion, vibrations

Citation Format(s)

Motor bearing fault diagnosis using trace ratio linear discriminant analysis. / Jin, Xiaohang; Zhao, Mingbo; Chow, Tommy W. S.; Pecht, Michael.

In: IEEE Transactions on Industrial Electronics, Vol. 61, No. 5, 6583974, 05.2014, p. 2441-2451.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review