DocumentCode :
1046327
Title :
Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring
Author :
Blödt, Martin ; Granjon, Pierre ; Raison, Bertrand ; Rostaing, Gilles
Volume :
55
Issue :
4
fYear :
2008
fDate :
4/1/2008 12:00:00 AM
Firstpage :
1813
Lastpage :
1822
Abstract :
This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.
Keywords :
fault diagnosis; induction motors; monitoring; rolling bearings; spectral analysis; stators; bearing damage detection; bearing fault detection; drive failures; induction motors; load torque variations; radial rotor movement; rolling-element bearing faults; spectral analysis; stator current monitoring; vibration monitoring; Airgap eccentricity; Induction motors; airgap eccentricity; bearing damage detection; induction motors; motor current signature analysis; spectral analysis; torque variations;
fLanguage :
English
Journal_Title :
Industrial Electronics, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0046
Type :
jour
DOI :
10.1109/TIE.2008.917108
Filename :
4438690
Link To Document :
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