Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic

This paper presents the implementation of broken rotor bar fault detection in an inverter-fed induction motor using motor current signal analysis (MCSA) and prognosis with fuzzy logic. Recently, inverter-fed induction motors have become very popular because of their adjustable speed drive. They have been used in many vital control applications such as rolling mills, variable speed compressors, pumps, and fans. The condition monitoring of these motors can significantly reduce the cost of maintenance in the early detection of faults. In this study, MCSA is applied to an inverter-fed induction motor to detect broken rotor bar faults. The diagnosis of a broken rotor bar fault in the squirrel cage induction motor, driven by an inverter, has been studied for stable, full load condition and has been carried out experimentally by analyzing the power spectrum density of the motor stator current. Motor stator currents are uploaded to a PC with the software of the inverter used and the current harmonics are obtained using LabVIEW for every fault condition. After extracting the characteristic frequencies of the broken rotor bar failure, a fuzzy logic algorithm is implemented for classifying the fault. Although there is much research on rotor bar faults for line-connected induction motors, there are no studies on the inverter-fed induction motor and fault diagnosis with fuzzy logic. The implementation results showed that the method is very efficient and useful for prognosis of the rotor faults.

[1]  L.A. Pereira,et al.  Rotor broken bar detection and diagnosis in induction motors using stator current signature analysis and fuzzy logic , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[2]  S. Seker,et al.  Continuous Wavelet Transform for Bearing Damage Detection in Electric Motors , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[3]  E.E. Yaz,et al.  A Reconfigurable Motor for Experimental Emulation of Stator Winding Interturn and Broken Bar Faults in Polyphase Induction Machines , 2008, IEEE Transactions on Energy Conversion.

[4]  F. Filippetti,et al.  AI techniques in induction machines diagnosis including the speed ripple effect , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[5]  W. T. Thomson,et al.  Online current monitoring for fault diagnosis in inverter fed induction motors , 1988 .

[6]  G. B. Kliman,et al.  Noninvasive detection of broken rotor bars in operating induction motors , 1988 .

[7]  Seydi Vakkas Üstün,et al.  Optimal tuning of PI coefficients by using fuzzy-genetic for V/f controlled induction motor , 2008, Expert Syst. Appl..

[8]  S. Seker.  Determination of air-gap eccentricity in electric motors using coherence analysis , 2000 .

[9]  Peter Vas,et al.  Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines , 1993 .

[10]  B. Martin.  PARAMETER ESTIMATION , 2012, Statistical Methods for Biomedical Research.

[11]  G.E. Dawson,et al.  The detection of broken bars in the cage rotor of an induction machine , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[12]  Mo-Yuen Chow,et al.  On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods , 2008, IEEE Transactions on Industrial Electronics.

[13]  Belle R. Upadhyaya,et al.  Fault detection based on continuous wavelet transform and sensor fusion in electric motors , 2009 .

[14]  M. Haji,et al.  Pattern Recognition-A Technique for Induction Machines Rotor Broken Bar Detection , 2001, IEEE Power Engineering Review.

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

[16]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[17]  O.F. Bay,et al.  Serial wound starter motor faults diagnosis using artificial neural network , 2004, Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM '04..

[18]  E.E. Yaz,et al.  A Reconfigurable Motor for Experimental Emulation of Stator Winding Inter-Turn and Broken Bar Faults in Polyphase Induction Machines , 2007, 2007 IEEE International Electric Machines & Drives Conference.

[19]  Emine Ayaz,et al.  Feature extraction related to bearing damage in electric motors by wavelet analysis , 2003, J. Frankl. Inst..

[20]  Abdülkadir Çakır,et al.  Rotor bar fault diagnosis in three phase induction motors by monitoring fluctuations of motor current zero crossing instants , 2007 .

[21]  Bo Li,et al.  Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[22]  Serhat Şeker,et al.  A Reliability Model for Induction Motor Ball Bearing Degradation , 2003 .

[23]  Ratna Dahiya,et al.  Rotor Faults Detection in Induction Motor by Wavelet Analysis , 2009 .

[24]  J. Lang,et al.  Detection of broken rotor bars in induction motors using state and parameter estimation , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[25]  Mehmet Akar,et al.  Detection of static eccentricity for permanent magnet synchronous motors using the coherence analysis , 2010 .

[26]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .