Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring
Top Cited Papers
- 4 April 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 55 (4), 1813-1822
- https://doi.org/10.1109/tie.2008.917108
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
This publication has 25 references indexed in Scilit:
- Bearing Fault Detection via Wavelet Packet Decomposition with Spectral Post Processing2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, 2007
- Fault Classification and Fault Signature Production for Rolling Element Bearings in Electric MachinesIEEE Transactions on Industry Applications, 2004
- Models for bearing damage detection in induction motors using stator current monitoringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Using rough sets techniques as a fault diagnosis classifier for induction motorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Neural-network-based motor rolling bearing fault diagnosisIEEE Transactions on Industrial Electronics, 2000
- A review of induction motors signature analysis as a medium for faults detectionIEEE Transactions on Industrial Electronics, 2000
- An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator currentIEEE Transactions on Industry Applications, 1999
- MODELING OF LOW SHAFT SPEED BEARING FAULTS FOR CONDITION MONITORINGMechanical Systems and Signal Processing, 1998
- AN ANALYTICAL MODEL FOR THE PREDICTION OF THE VIBRATION RESPONSE OF ROLLING ELEMENT BEARINGS DUE TO A LOCALIZED DEFECTJournal of Sound and Vibration, 1997
- Model for the vibration produced by a single point defect in a rolling element bearingJournal of Sound and Vibration, 1984