IEEE Transactions on Industrial Electronics

Detection of Generalized-Roughness and Single-Point Bearing Faults Using Linear Prediction-Based Current Noise Cancellation


Fardin Dalvand1, Myeongsu Kang2, Satar Dalvand3, and Michael G. Pecht 2
1 Iranian Oil Pipeline and Telecommunication Company, Khorramabad
2 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
3 Ava Dal Sanat Company, Lorestan Province Science and Technology Park, Khorramabad

Abstract:

The dominant components in the stator current of a typical induction motor contain a substantial amount of information that is not related to bearing faults and can be considered as "noise" to bearing fault detection. The presence of the noise in the current signal creates the possibility of missed and false alarms. This study develops a current noise cancellation method. When noise cancellation is used for recovering a desired signal, the distinguishing characteristics of both the desired signal and the noise need to be considered. Contrary to conventional current noise cancellation methods, "predictability" is considered as the distinguishing characteristic. Linear prediction theory with an optimum prediction order is applied to efficiently model the predictable components as noise. The developed method is able to efficiently cancel out the noise from the current signal. The information about the mechanical load level, the faulty part of the bearing, machine parameters, nameplate values, the current spectrum distribution, and the current data associated with the healthy bearing is not required in this method. Simulation and experimental results confirm the merits and effectiveness of the developed method to detect the bearing fault types through kurtosis and spectral analysis.

This article is available online here and to CALCE Consortium Members for personal review.

© 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 © 2018 by CALCE and the University of Maryland, All Rights Reserved