Bearing Fault Detection in Induction Machine Based on Stator Current Spectrum Monitoring

Bearing faults account for a large majority of the faults in a three-phase induction motor. Recently, many research activities were focused on the diagnosis of bearing faults by motor current signature analysis. However, the effective frequency band is not fully understood in terms of the diagnosis of the bearing fault. Moreover, the temporal change in motor stator current spectrum with faulty bearing has not been sufficiently investigated. The purpose of this paper is to clarify the characteristic frequency band and to evaluate the temporal change in the power spectral density of stator current by using powder contaminated bearing before complete halt of the motor. Experiments were performed with normal and powder contaminated bearings in the induction motor. The diagnosis technique for the powder contaminated bearing is discussed based on the experimental results.

[1]  Bertrand Raison,et al.  Models for bearing damage detection in induction motors using stator current monitoring , 2008, 2004 IEEE International Symposium on Industrial Electronics.

[2]  Michael J. Devaney,et al.  Bearing damage detection via wavelet packet decomposition of the stator current , 2004, IEEE Transactions on Instrumentation and Measurement.

[3]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[4]  Mohamed El Hachemi Benbouzid.  A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..

[5]  S. J. Lacey,et al.  An Overview of Bearing Vibration Analysis , 2008 .

[6]  Bo-Suk Yang,et al.  Intelligent fault diagnosis system of induction motor based on transient current signal , 2009 .

[7]  Norman Mariun,et al.  Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review , 2011 .

[8]  G. S. Maruthi,et al.  Experimental investigation on detection of air gap eccentricity in induction motors by current and vibration signature analysis using non-invasive sensors , 2012 .

[9]  Javad Poshtan,et al.  An advanced Park's vectors approach for bearing fault detection , 2009 .

[10]  T.G. Habetler,et al.  Motor bearing damage detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[11]  Izzet Yilmaz,et al.  Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study , 2008 .

[12]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009 .

[13]  T.G. Habetler,et al.  Fault classification and fault signature production for rolling element bearings in electric machines , 2004, IEEE Transactions on Industry Applications.

[14]  P. Granjon,et al.  Bearing Fault Diagnosis in Induction Machine Based on Current Analysis Using High-Resolution Technique , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[15]  Thomas G. Habetler,et al.  Effects of time-varying loads on rotor fault detection in induction machines , 1993 .

[16]  Bong-Hwan Kwon,et al.  Online Diagnosis of Induction Motors Using MCSA , 2006, IEEE Transactions on Industrial Electronics.