IEEE Transactions on Industrial Electronics, Vol. 61, No. 5, May 2014

Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis

Xiaohang Jin, Mingbo Zhao, Tommy W. S. Chow, Senior Member, IEEE, and Michael Pecht, Fellow, IEEE


Bearings are critical components in induction motors and brush-less 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.

Complete article available from the publishers and to the CALCE consortium members.

© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

[Home Page] [Articles Page]
Copyright © 2014 by CALCE and the University of Maryland, All Rights Reserved