Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지

Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum

  • Sadoughi, Alireza (Dept. of Electrical and Computer Eng., Isfahan University of Technology) ;
  • Ebrahimi, Mohammad (Dept. of Electrical and Computer Eng., Isfahan University of Technology) ;
  • Moallem, Mehdi (Dept. of Electrical and Computer Eng., Isfahan University of Technology) ;
  • Sadri, Saeid (Dept. of Electrical and Computer Eng., Isfahan University of Technology)
  • Published : 2008.07.20
Download PDF
⟨ Previous Next ⟩

Abstract

Many induction motor broken bar diagnosis methods are based on evaluating special components in machine signals spectrums. Current, power, flux, etc are among these signals. Frequencies related to a broken rotor fault are slip dependent, therefore, correct diagnosis of fault - especially when obtrusive frequency components are present - depends on accurate determination of motor velocity and slip. The traditional methods typically require several sensors that should be pre-installed in some cases. This paper presents a diagnosis method based on only a vibration sensor. Motor velocity oscillation due to a broken rotor causes frequency components at twice slip frequency difference around speed frequency in vibration spectrum. Speed frequency and its harmonics as well as twice supply frequency, can easily and accurately be found in a vibration spectrum, therefore th motor slip can be computed. Now components related to rotor fault can be found. It is shown that a trained neural network - as a substitute for an expert person - can easily categorize the existence and the severity of a fault according to the features extracted from the presented method. This method requires no information about th motor internal and has been able to diagnose correctly in all the laboratory tests.

Keywords

References

  1. Benbouzid M.E.H., "A review of induction motors signature analysis as a medium for fault detection", IEEE Trans.Ind Electron., Vol. 47, No. 5, pp. 984-993, Oct. 2000 https://doi.org/10.1109/41.873206
  2. Nandi S., Toliyat H., Li X., "Condition monitoring and fault diagnosis of electrical motors-a review", IEEE Trans. Energy Convers., Vol. 20, No. 4, pp. 719-729, Dec. 2005 https://doi.org/10.1109/TEC.2005.847955
  3. Elkasabgy N. M. ,Eastham A. R.,Dawson G.E., "Detection of broken bars in the cage rotor on an induction machine", IEEETrans.Ind.Applns.,Vol. IA-22, No.6, pp.165-171, 1992
  4. Deleroi W., "Squirrel cage motor with broken bar in the Rotor -Physical Phenomena and their experimental assessment", ICEM, Budapest, pp. 767-770, 1982
  5. Filippetti F., Franceschini G. ,Tassoni C.,Vas P., "A technique in induction machines diagnosis including the speed ripple effect", IEEE - IAS annual Meeting Conference, San Diengo, pp. 655-662, Oct 6-10,1996
  6. Cardoso A. J. M., Crus S. M. A., Carvalho J.F.S., Saraiva E. S., "Rotor cage fault diagnosis in three phase induction motors, by Park's vector approach", IEEE IAS 95, pp. 642-646, 1995
  7. Gaydon B.G., "An instrument to detect induction motor rotor circuit defects by speed fluctuation measurements", Electric Test and Measuring Instrumentation, Testmex 79 Conference Papers, pp. 5-8, 1979
  8. Legowski S.F.,Trzynadlowski A., "Instantaneous stator power as a medium for the signature analysis of induction motors", IEEE IAS9, pp. 619-624, 1995
  9. Liu Z., Yin X., Zhang Z., Chen D., ChenW., "Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors", IEEE Trans. on Energy Conversion, Vol. 19, No. 3, pp. 485-490, 2004 https://doi.org/10.1109/TEC.2004.832052
  10. Kliman G. B., Stein J. Endicott R. D., Madden M. W., "Non-invasive detection of broken rotor bars in operating induction motors", IEEE Trans. Energy Conversion, Vol. EC-3, No. 4, pp. 873-879, Dec. 1998
  11. Bellini A., Filippetti F., Franceschini G., Tassoni C., Kliman G., "Quantitative evaluation of induction motor broken bars by means of electrical signature analysis", IEEE Trans. Ind. Applications, Vol. 37, No. 5, pp. 1248- 1255, Sep/Oct. 2001 https://doi.org/10.1109/28.952499
  12. M. Ebrahimi, A. Sadoughi, M. Bayat, "Induction motor broken bar diagnosis by simultaneous current and vibration sampling", XVII International Conference on Electrical Machines, ICEM2006, Greece. Sept. 2006
  13. A. Sadoughi, M. Ebrahimi, E. Rezaei, "A New Approach for Induction Motor Broken Bar Diagnosis by Using Vibration Spectrum", SICE-ICASE International Joint Conference 2006, Bexco, Busan, Korea, pp. 4715-4720, Oct. 18-21, 2006
  14. M. Rokonuzzaman and M.A. Ranman, "Neural network based incipient fault detection of induction motors", IEEE-IAS, pp. 199-202, 1995
  15. F. Filippetti and G. Franceschini, "Neural networks aided on-line diagnostics of induction motor rotor faults", IEEE Trans. On Industry Applications, Vol. 31, No. 4, pp. 892-899, Jul./Aug. 1995 https://doi.org/10.1109/28.395301
  16. M.Y. Chow, "Design considerations for a motor fault detection artificial neural network", IEEE, Industrial Electronics, Control, Instrumentation, and Automation Conference, Vol. 3, pp. 1455-1459, Nov. 1992
  17. M. Moradian, M. Ebrahimi, M. Danesh, and M. Bayat, "Detection of broken bars in induction motors using a neural network", Journal of Power Electronics, Korean Institute of Power Electronics, pp. 245-252, July 2006
  18. Thomson w. T., Dahlieh M. D., Theory of Vibration with Applications
  19. Lalanne M., Ferraris G., Rotordynamics Prediction in Engineering, 2nd edition, John Wiley and Sons, 1998
  20. Oppenheim A. V., Schafer R.W., Discrete - Time Signal Processing, Prentic-Hall, 1989
  21. Willi ams A.B., Taylor F.J., Electronic Filter Design Hand Book, 3rd ed., 1995
  22. Kartalopoulos S. V., Understanding Neural Networks and Fuzzy Logic- Basic Concepts and Applications, IEEE press, 1996
  23. Menhaj M. B., Computational Intelligence (vol.1), Fundamentals of Neural Networks, Amirkabir Univ. Press, 2000