Efficiency Analysis of Submersible Induction Motor with Broken Rotor Bar

Conference paper


This study analyzes effects of squirrel cage faults on submersible induction motors efficiency at steady-state condition. There are a lot of studies about effects of the cage faults on motor performance. Especially, the effects of the cage faults on the motor parameters such as current, torque and speed are well known. Unlike the literature, cage fault effects on efficiency are analyzed in this study. Furthermore, fluctuations and mean value changes resulting from the rotor faults are ranked according to size of these faults. Healthy and five different faults were investigated by using 10, 25, 30 and 50 HP submersible induction motors in both simulations and experiments. Time stepping finite element method solution was used to compute motor quantities in the simulation. Good agreement was achieved between simulation and experimental results. The effects of rotor faults on motor efficiency were clearly ranked according to size of faults.


Efficiency analysis Energy efficiency Finite element method Induction motors Rotor faults Squirrel cage 


  1. 1.
    A.H. Bonnett, G.C. Houkup, Cause and analysis of stator and rotor faults in three-phase squirrel-cage induction motors. IEEE Trans. Ind. Appl. 28(4), 921–937 (1992)CrossRefGoogle Scholar
  2. 2.
    J. Penman, A. Stavrou, Broken rotor bars their effect on the transient performance of induction machines. IEE Proc. Electr. Power Appl. 143(6), 449–457 (1996)CrossRefGoogle Scholar
  3. 3.
    X. Ying, Characteristic performance analysis of squirrel cage induction motor with broken bars. IEEE Trans. Magn. 45(2), 759–766 (2009)CrossRefGoogle Scholar
  4. 4.
    H. Arabacı, O. Bilgin, Analysis of rotor faults effects on submersible induction motor’ efficiency, in Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS 2013, pp. 265–270, San Francisco, USA, 23–25 Oct 2013Google Scholar
  5. 5.
    H. Arabacı, O. Bilgin, Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Comput. Appl. 19(5), 713–723 (2010)CrossRefGoogle Scholar
  6. 6.
    M. Haji, H.A. Toliyat, Pattern recognition: a technique for induction machines rotor fault detection ‘Eccentricity and broken bar fault’. IEEE Ind. Appl. Conf. Thirty-Sixth IAS Annu. Meet. 3, 1572–1578 (2001)Google Scholar
  7. 7.
    G. Didier, E. Ternisien, O. Caspary, H. Razik, Fault detection of broken rotor bars in induction motor using a global fault index. IEEE Trans. Ind. Appl. 42(1), 79–88 (2006)CrossRefGoogle Scholar
  8. 8.
    M. Chow, S.O. Yee, Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks. IEEE Trans. Energy Convers. 6(3), 536–545 (1991)CrossRefGoogle Scholar
  9. 9.
    H. Arabaci, O. Bilgin, Neural network classification and diagnosis of broken rotor bar faults by means of short time fourier transform, in International MultiConference of Engineers and Computer Scientists, Hong Kong, pp. 219–223 (2009)Google Scholar
  10. 10.
    R.R. Schoen, T.G. Habetler, Effects of time-varying loads on rotor fault detection in induction machines. IEEE Trans. Ind. Appl. 31(4), 900–906 (1995)CrossRefGoogle Scholar
  11. 11.
    R. Casimir, E. Boutleux, G. Clerc, F. Chappuis, Broken bars detection in an induction motor by pattern recognition, in IEEE Bologna PowerTech Conference, Bologna, Italy (2003)Google Scholar
  12. 12.
    P.J.C. Branco, J.A. Dente, R.V. Mendes, Using immunology principles for fault detection. IEEE Trans. Industr. Electron. 50(2), 362–373 (2003)CrossRefGoogle Scholar
  13. 13.
    B. Yazici, G.B. Kliman, An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current. IEEE Trans. Ind. Appl. 35(2), 442–452 (1999)CrossRefGoogle Scholar
  14. 14.
    M.E.H. Benbouzid, G.B. Klimam, What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Trans. Energy Convers. 18(2), 238–244 (2003)CrossRefGoogle Scholar
  15. 15.
    J. Cusido, L. Romeral, J.A. Ortega, J.A. Rosero, A.G. Espinosa, Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans. Industr. Electron. 55(2), 633–643 (2008)CrossRefGoogle Scholar
  16. 16.
    L. Sun, H. Li, B. Xu, A hybrid detection method of broken rotor bars in cage induction motors, in International Conference on Power System Technology, Singapore, pp. 177–181 (2004)Google Scholar
  17. 17.
    B. Ayhan, M. Chow, M. Song, Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors. IEEE Trans. Energy Convers. 20(2), 336–343 (2005)CrossRefGoogle Scholar
  18. 18.
    K. Kim, A.G. Parlos, R.M. Bharadwaj, Sensorless fault diagnosis of induction motors. IEEE Trans. Industr. Electron. 50(5), 1038–1051 (2003)CrossRefGoogle Scholar
  19. 19.
    A. Widodo, B.S. Yang, Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21(6), 2560–2574 (2007)CrossRefGoogle Scholar
  20. 20.
    S. Günal, D.G. Ece, Ö.N. Gerek, Induction machine condition monitoring using notch-filtered motor current. Mech. Syst. Signal Process. 23(8), 2658–2670 (2009)CrossRefGoogle Scholar
  21. 21.
    I. Aydin, M. Karakose, And Akin E., Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection. Expert Syst. Appl. 37(7), 5285–5294 (2010)CrossRefGoogle Scholar
  22. 22.
    Y. Lei, Z. He, Y. Zi, Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 23(4), 1327–1338 (2009)CrossRefGoogle Scholar
  23. 23.
    M. Eltabach, J. Antoni, G. Shanina, S. Sieg-Zieba, X. Carniel, Broken rotor bars detection by a new non-invasive diagnostic procedure. Mech. Syst. Signal Process. 23(4), 1398–1412 (2009)CrossRefGoogle Scholar
  24. 24.
    A.Y. Ben Sasi, F. Gu, Y. Li, A.D. Ball, A validated model for the prediction of rotor bar fault in squirrel-cage motors using instantaneous angular speed. Mech. Syst. Signal Process. 20, 1572–1589 (2006)CrossRefGoogle Scholar
  25. 25.
    H. Su, T. Chong, Induction machine condition monitoring using neural network modeling. Trans. Industr. Electron. 54(1), 241–249 (2007)CrossRefGoogle Scholar
  26. 26.
    S. Nandi, R.M. Bharadwaj, H.A. Toliyat, Performance analysis of a three-phase induction motor under mixed eccentricity condition. IEEE Trans. Energy Convers. 17(3), 392–399 (2002)CrossRefGoogle Scholar
  27. 27.
    X. Li, Q. Wu, Performance analysis of a three-phase induction machine with inclined static eccentricity. IEEE Trans. Ind. Appl. 43(2), 531–541 (2007)CrossRefGoogle Scholar
  28. 28.
    X. Luo, Y. Liao, H.A. Toliyat, A. El-Antably, T.A. Lipo, Multiple couple circuit modelling of induction machines. IEEE Trans. Ind. Appl. 31(4), 311–318 (1995)Google Scholar
  29. 29.
    P.D. Agarwal, Equivalent circuits and performance calculations of canned motors, on power apparatus and systems part-III. Trans. Am. Inst. Electr. Eng. 79(3), 635–642 (1960)Google Scholar
  30. 30.
    M. Ito, N. Fujimoto, H. Okuda, N. Takahashi, T. Miyata, Analytical model for magnetic field analysis of induction motor performance. IEEE Trans. Power Apparatus Syst. PAS 100(11), 4582–4590 (1981)CrossRefGoogle Scholar
  31. 31.
    P. Vas, Steady state and transient performance of induction motors with rotor asymmetry. IEEE Trans. Power Apparatus Syst. PAS 101(9), 3246–3251 (1982)CrossRefGoogle Scholar
  32. 32.
    P. Baldassari, N.A. Demerdash, A combined finite element-state space modeling environment for induction motors in the ABC frame of reference: the blocked-rotor and sinusoidally energized load conditions. IEEE Trans. Energy Convers. 7(4), 710–720 (1992)CrossRefGoogle Scholar
  33. 33.
    J.F. Bangura, N.A. Demerdash, Diagnosis and characterization of effects of broken bars and connectors in squirrel-cage induction motors by a time-stepping coupled finite element-state space modeling approach. IEEE Trans. Energy Convers. 14(4), 1167–1176 (1999)CrossRefGoogle Scholar
  34. 34.
    J. Faiz, B.M. Ebrahimi, Signature analysis of electrical and mechanical signals for diagnosis of broken rotor bars in an induction motor. Electromagnetics 27, 507–526 (2007)CrossRefGoogle Scholar
  35. 35.
    W. Li, Y. Xie, J. Shen, Y. Luo, Finite-element analysis of field distribution and characteristic performance of squirrel-cage induction motor with broken bars. IEEE Trans. Magn. 43(4), 1537–1540 (2007)CrossRefGoogle Scholar
  36. 36.
    J. Faiz, B.M. Ebrahimi, Locating rotor broken bars in induction motors using finite element method. Energy Convers. Manage. 50, 125–131 (2009)CrossRefGoogle Scholar
  37. 37.
    K. Kurihara, T. Kubota, M. Hori, Steady-state and transient performance analysis for a single-phase capacitor-run permanent-magnet motor with skewed rotor slots. IEEE Trans. Industr. Electron. 57(1), 44–51 (2010)CrossRefGoogle Scholar
  38. 38.
    J. Faiz, B.M. Ebrahimi, B. Akin, H.A. Toliyat, Finite-element transient analysis of induction motors under mixed eccentricity fault. IEEE Trans. Magn. 44(1), 66–74 (2008)CrossRefGoogle Scholar
  39. 39.
    J. Sprooten, J.C. Maun, Influence of saturation level on the effect of broken bars in induction motors using fundamental electromagnetic laws and finite element simulations. IEEE Trans. Energy Convers. 24(3), 557–564 (2009)CrossRefGoogle Scholar
  40. 40.
    O.A. Mohammed, N.Y. Abed, S. Ganu, Modeling and characterization of induction motor internal faults using finite-element and discrete wavelet transforms. IEEE Trans. Magn. 42(10), 3434–3436 (2006)CrossRefGoogle Scholar
  41. 41.
    T.W. Preston, A.B.J. Reece, P.S. Sangha, Induction motor analysis by time-stepping techniques. IEEE Trans. Magn. 24(1), 471–474 (1988)CrossRefGoogle Scholar
  42. 42.
    J. Faiz, B.M. Ebrahimi, A new pattern for detecting broken rotor bars in induction motors during start-up. IEEE Trans. Magn. 44(12), 4673–4683 (2008)CrossRefGoogle Scholar
  43. 43.
    D.G. Dorrell, P.J. Holik, P. Lombard, H.J. Thougaard, F. Jensen, A multisliced finite-element model for induction machines incorporating interbar current. IEEE Trans. Ind. Appl. 45(1), 131–141 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical and Electronics Engineering, Technology FacultySelcuk UniversityKonyaTurkey
  2. 2.Department of Electrical and Electronics Engineering, Engineering FacultySelcuk UniversityKonyaTurkey

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