Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study

This paper deals with the problem of bearing failure detection and diagnosis in induction motors. Indeed, bearing deterioration is now the main cause of induction motor rotor failures. In this context, two fault detection and diagnosis techniques, namely the Park transform approach and the Concordia transform, are briefly presented and compared. Experimental tests, on a 0.75 kW two-pole induction motor with artificial bearing damage, outline the main features of the aforementioned approaches for small- and medium-size induction motors bearing failure detection and/or diagnosis.

[1]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[2]  A.J.M. Cardoso,et al.  Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[3]  Mo-Yuen Chow Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection , 1998 .

[4]  Robert X. Gao,et al.  Wavelet transform with spectral post-processing for enhanced feature extraction , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[5]  A.J. Marques Cardoso,et al.  Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[6]  Mohamed Benbouzid,et al.  A review of induction motors signature analysis as a medium for faults detection , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[7]  Makarand Sudhakar Ballal,et al.  Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor , 2007, IEEE Transactions on Industrial Electronics.

[8]  J. Ilonen,et al.  Diagnosis tool for motor condition monitoring , 2005, IEEE Transactions on Industry Applications.

[9]  T. A. Harris,et al.  Rolling Bearing Analysis , 1967 .

[10]  A.M. Knight,et al.  Mechanical fault detection in a medium-sized induction motor using stator current monitoring , 2005, IEEE Transactions on Energy Conversion.

[11]  Robert X. Gao,et al.  Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring] , 2003, IEEE Trans. Instrum. Meas..

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

[13]  Mohamed Benbouzid,et al.  Induction Motors Bearing Failures Detection and Diagnosis Using a RBF ANN Park Pattern Based Method , 2006 .

[14]  Mohamed Benbouzid,et al.  Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system , 2003 .

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

[16]  Thomas G. Habetler,et al.  An amplitude Modulation detector for fault diagnosis in rolling element bearings , 2004, IEEE Transactions on Industrial Electronics.

[17]  D. Diallo,et al.  Fault detection and diagnosis in an induction Machine drive: a pattern recognition approach based on concordia stator mean current vector , 2005, IEEE Transactions on Energy Conversion.

[18]  Antonio J. Marques Cardoso,et al.  Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's Vector approach , 1997 .

[19]  Thomas G. Habetler,et al.  Bearing fault detection via autoregressive stator current modeling , 2003 .

[20]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 2000 .

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

[22]  Demba Diallo,et al.  A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive , 2008, IEEE Transactions on Industrial Electronics.

[23]  Birsen Yazici,et al.  An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current , 1999 .

[24]  M. E. H. Benbouzid,et al.  What Stator Current Processing Based Technique to Use for Induction Motor Rotor Faults Diagnosis , 2002, IEEE Power Engineering Review.

[25]  L. Eren,et al.  Detecting motor bearing faults , 2004, IEEE Instrumentation & Measurement Magazine.

[26]  Alexander G. Parlos,et al.  Sensorless fault diagnosis of induction motors , 2003, IEEE Trans. Ind. Electron..

[27]  F. Zidani,et al.  Fuzzy Detection and Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor , 2005, IEEE International Conference on Electric Machines and Drives, 2005..

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

[29]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[30]  M.J. Melfi,et al.  Bearing current remediation options , 2004, IEEE Industry Applications Magazine.

[31]  N. Tandon,et al.  A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings , 2007 .