Research Journal of Engineering and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Detection of Faults in Induction Motors Using Embedded Zero-tree Wavelet Analysis of Stator Current Signals


Affiliations
1 Department of Electrical Engineering, University College of Engineering, Rajasthan Technical University (RTU), Kota (RAJ.), India
     

   Subscribe/Renew Journal


This paper discusses the detection of commonly occurring electrical and mechanical faults in three-phase induction motor. Besides, it explicates the underlying tenet, causes and consequences of frequently occurring faults in induction motors. The paper attempts to discuss different types of induction motor faults, their causes and detection techniques. It is found that the detection techniques which evaluate the dynamic behavior of the signal (such as Wavelet Transform analysis) are best suited for the purpose. The Embedded Zero-tree Wavelet (EZW) technique relieves from the dreary handling of the bulk data by reducing the dimensionality of the data. The competence of the EZW algorithm is expounded through implementation of the scheme on the stator current signals derived during electrical and mechanical faults in Induction motors. The experimentation to corroborate the scheme has been performed on a 3-, 1.5 kW, 4P, 1440 RPM, ABB squirrel cage motor. The dSPACE DS 1104 DSP control card (based on TMS 320F240 DSP), dSPACE controldesk software and the MATLAB software have been used as the data acquisition tool and programming platform.

Keywords

Fault Detection and Identification (FDI), Mechanical and Electrical Faults, Condition Monitoring.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Devaney M. J. and L. Eren, “Detecting Motor Bearing Faults”, IEEE Instrumentation and Measurenent Magazine, Pp. 30-50, December 2004.

  • Faiz J. and B. M. Ebrahimi, “Mixed Fault Diagnosis in Threephase Squirrel-Cage Induction Motor Using Analysis of Airgap Magnetic Field”, Progress in Electro-magnetics Research, PIER 64, Pp. 239-355, 2006.

  • Han Y. and Y. H. Song, “Condition Monitoring Techniques for Electrical Equipment-A Literature Survey”, IEEE Transaction on Power Delivery, Vol. 18, No. 1, January 2003.

  • Nandi S. et. al.,” Condition Monitoring and Fault Diagnosis of Electrical Motors- A Review,” Proceedings of 34th IAS annual meeting on Industry Applications, IEEE, Vol. 3, Pp. 197-204, 3-7 Oct 1999.

  • Sin M. L. et. al., “Induction Machine On-line Condition Monitoring and Fault Diagnosis – A survey”, Proceedings of the Australasian Universities Power -------, 2003/itee.uq.edu.au.

  • Arthur N. and J. Penman, “ Induction Machine Condition Monitoring with higher order spectra”, IEEE transaction on Industrial Applications, Vol.47, No.5, Pp. 1031-1041, October 2000.

  • Bellini A. et. al., “Monitoring of Induction Machines by Maximum Covarience Method for Frequency Tracking”, Conference Records of the 2004 IEEE Industry Applications Conference, 39th Annual Meeting, Vol. 2, Pp. 743-749, 3-7 Oct. 2004.

  • Bellini A. et. al., “Diagnosis of Induction Machines by d-q and i.s.c Rotor Models”, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Vienna, Austria, 7-9 September 2005.

  • Riley C. M. et. al., “Stator Current Harmonics And Their Causal Vibrations: A Preliminary Investigation of Sensorless Vibration Monitoring Applications”, IEEE Transaction on Industry applications, Vol. 35, no. 1, Pp. 94-99, Jan/Feb 1999.

  • Chow Mo-yuen et. al., “A Neural Network Approach to Real- Time Condition Monitoring of Induction Motors”, IEEE Transactions on Industrial Electronics, Vol. 38, No. 6, December 1991.

  • Eren L. et. al., “Neural Network Based Bearing Fault Detection”, Instrumentation and Measurement Technology Conference, Como, Italy, 18-20 May 2004, Pp. 1657-1660.

  • Kim K. and A. G. Parlos. “Induction Motor Fault Diagnosis based on Neuropredictors and Wavelet Signal Processing”, IEEE transaction on Mechatronics, Vol. 7, No. 2, Pp. 201- 219, June 2002.

  • Hsu John S., “Monitoring of Defects in Induction Motors Through Air-Gap Torque Observation”, IEEE Transactions on Industry Applications, Vol. 31, no.5, Pp. 1016-1021, September/October 1995.

  • Arthur N. et. al., “Induction Machine Condition Monitoring with higher order spectra, Part II: Variable frequency operation and automated diagnosis”, IECON’98, Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, Pp. 1895- 1900, Vol. 3, 31 Aug.-4 Sept. 1998 .

  • Dorrell D. G. et. al.. “Analysis of air gap flux, current and vibration signals as a function of the combination of static and dynamic air gap eccentricity in 3-phase induction motors”, IEEE transaction on Industrial Applications, Vol.33, No.1, Pp. 24-34, Jan./Feb 1997.

  • Riley C. M. et. al., “A Method for Sensorless On-Line Vibration Monitoring of Induction Machines”, IEEE Transaction on Industry applications, Vol. 34, no. 6, Pp. 1240-45, Nov/Dec 1998.

  • Riley C. M. et. al., “A Method for Sensorless On-Line Vibration Monitoring of Induction Machines”, IEEE Industry Applications Society Annual Meeting Conference Record, Pp. 201-207, 1997.

  • Schoen R. R. and T. G. Habetler, ”Evaluation and Implementation of a System to Eliminate Arbitrary Load Effects in Current-Based Monitoring of Induction machines”, IEEE Transaction on Industrial Applications, Vol. 33, No. 6, Pp. 1571- 77, Nov/Dec. 1997.

  • Nejjari H. and M. E. H. Benbouzid, “Application of Fuzzy Logic to Induction Motors Condition Monitoring”, IEEE Power Engineering Review, Pp. 52-54, June 1999.

  • Magnago F. and A. Abur,” Fault location using wavelets”, IEEE Transactions on Power Delivery, 13(4), 1475-1480, 1998.

  • Zanardelli W. G. et. al., “Wavelet Based Methods for the Prognosis of Mechanical and Electrical Failures in Electric Motors”, Mechanical Systems and Signal Processing (Elsavier), Pp 411-426, 2005.

  • Zanardelli W. G., and E. G. Strangas, “Methods to Identify Intermittent Electrical and Mechanical Faults in Permanent Magnet AC Drives Based on Wavelet Analysis”, IEEE Conference on Vehicle Power and Propulsion, 7-9 Sept. 2005.

  • Albrecht P.F. et. al., ”Assessment of the reliability of motors in utility applications-Updated,” IEEE Transactions on Energy conversion, Vol. 20, Pp. 719-729, 2005.

  • O’Donnell P., ”Report of Large Motor Reliability Survey of Industrial and Commercial Installations”, Part I and II, Motor Reliability Working Group, IEEE Industry Application Society (IAS), IEEE Transaction on Industry Applications, Pp 853-872, July/August 1985; Part III, Motor Reliability Working Group, IEEE Industry Application Society (IAS), IEEE Transaction on Industry Applications, Pp 153-158, January/February 1985.

  • Benbouzid M.E.H.,” A Review of Induction Motors Signature Analysis as a medium of faults detection,” IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, Oct. 2000.

  • Benbouzid M.E.H. et. al. “Induction motors’ Faults Detection and Localization Using Stator Current Advanced Signal processing Techniques”, IEEE Transactions on Power Electronics, Vol. 14, No.1, Pp. 14-22, January 1999.

  • Awadallah M. A. and M. M. Marcos, “Application of AI Tools in Fault Diagnosis of Electrical Machines and Drives – An Overview”, IEEE Transactions on Energy Conversion, Vol. 18, No. 2, June 2003. 28. Bonnett A. H. and George C. Soukup, “Cause and Analysis of Stator and Rotor Failures in Three-Phase Squirrel-Cage Induction Motors”, IEEE Transactions on Industry Applications, Vol. 28, No. 4, July/August 1992.

  • Barendse P. S. and P.Pillay, “The Detection of Unbalanced Faults in Inverter-Fed Induction Machines”, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Pp. 46-51, 6-8 september,2007.

  • Kim K. and A. G. Parlos. “Reducing the Impact of False Alarms in Induction Motor Fault Daignosis”, Journal of Dynamic Systems, Measurement, and Control, Transactions of the ASME, Vol. 125, Pp. 80- 95, March 2003.

  • Khan M. A. S. K. et. al., “Real –Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three- Phase Induction Motors”,IEEE Transactions on Energy Conversion, Vol. 22, No.3, Pp. September 2007.

  • J.M.Shapiro,”Embedded Image Coding Using Zerotrees of Wavelet Coefficients”, IEEE transactions on Signal Processing, Vol. 41, Dec. 1993, Pp. 3445-3462.

  • Y.Y.Hu et. al., “Embedded Wavelet Image Compression Algorithm Based on Full Zerotree”, International Journal of Computer sciences and Engg. Systems, Vol. I, No.2, April 2007, Pp. 131-138.

  • Ilonen J. et. al., ”Diagnosis tool for motor condition monitoring”, IEEE Transaction on Industrial Applications, Vol. 41, No.4, Pp. 963-967, 2005.


Abstract Views: 854

PDF Views: 0




  • Detection of Faults in Induction Motors Using Embedded Zero-tree Wavelet Analysis of Stator Current Signals

Abstract Views: 854  |  PDF Views: 0

Authors

Shashi Raj Kapoor
Department of Electrical Engineering, University College of Engineering, Rajasthan Technical University (RTU), Kota (RAJ.), India

Abstract


This paper discusses the detection of commonly occurring electrical and mechanical faults in three-phase induction motor. Besides, it explicates the underlying tenet, causes and consequences of frequently occurring faults in induction motors. The paper attempts to discuss different types of induction motor faults, their causes and detection techniques. It is found that the detection techniques which evaluate the dynamic behavior of the signal (such as Wavelet Transform analysis) are best suited for the purpose. The Embedded Zero-tree Wavelet (EZW) technique relieves from the dreary handling of the bulk data by reducing the dimensionality of the data. The competence of the EZW algorithm is expounded through implementation of the scheme on the stator current signals derived during electrical and mechanical faults in Induction motors. The experimentation to corroborate the scheme has been performed on a 3-, 1.5 kW, 4P, 1440 RPM, ABB squirrel cage motor. The dSPACE DS 1104 DSP control card (based on TMS 320F240 DSP), dSPACE controldesk software and the MATLAB software have been used as the data acquisition tool and programming platform.

Keywords


Fault Detection and Identification (FDI), Mechanical and Electrical Faults, Condition Monitoring.

References