Journal of Failure Analysis and Prevention

, Volume 13, Issue 3, pp 346–352 | Cite as

Approach Signal for Rotor Fault Detection in Induction Motors

Technical Article---Peer-Reviewed

Abstract

In this paper, two approach signals are used for broken rotor bar fault diagnosis. One is based on the spectrum analysis, such as the fast Fourier transform, which utilizes the steady-state spectral components of the stator quantities. The accuracy of this technique depends on the loading conditions and constant speed of the machine. The second approach is based on the discrete wavelet transform which is considered an ideal tool for this purpose due to its suitability for the analysis of signals, the frequency spectrum of which is variable in time. These two approaches are tested in simulation and validated experimentally.

Keywords

Induction motors Broken rotor bars Fast Fourier transform (FFT) Discrete wavelet transform (DWT) Fault diagnosis 

References

  1. 1.
    Ayhan, B., Trussell, H.J., Chow, M.Y., Song, M.H.: On the use of a lower sampling rate for broke rotor bar detection with DTFT and AR-based spectrum methods. IEEE Trans. Ind. Electron. 55(3), 1421–1434 (2008)CrossRefGoogle Scholar
  2. 2.
    Bachir, S., Tnani, S., Trigeassou, J.C., Champenois, G.: Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transit. Ind. Electron. 53(3), 963–973 (2006)CrossRefGoogle Scholar
  3. 3.
    Bellini, A., Filippetti, F., Tassoni, C., Capolino, G.A.: Advances in diagnostic techniques for induction machines. IEEE Trans. Ind. Electron. 55(12), 4109–4125 (2008)CrossRefGoogle Scholar
  4. 4.
    da Silva, A.M., Povinelli, R.J., Demerdash, N.A.O.: Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Trans. Ind. Electron. 55(3), 1310–1318 (2008)CrossRefGoogle Scholar
  5. 5.
    Bossio, G.R., De Angelo, C.H., Bossio, J.M., Pezzani, C.M., García, G.O.: Separating broken rotor bars and load oscillations on IM fault diagnosis through the instantaneous active and reactive currents. IEEE Trans. Ind.Electron. 56(11), 4571–4580 (2009)CrossRefGoogle Scholar
  6. 6.
    Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., Rezzoug, A.: Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans. Ind. Electron. 58(9), 4385–4395 (2011)CrossRefGoogle Scholar
  7. 7.
    Sadeghian, A., Ye, Z., Wu, B.: Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans. Instrumen. Meas. 58(7), 2253–2263 (2009)CrossRefGoogle Scholar
  8. 8.
    Cusidó, J., Romeral, L., Ortega, J.A., Rosero, J.A., Garcia Espinosa, A.: Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans. Ind. Electron. 55(3), 633–643 (2008)CrossRefGoogle Scholar
  9. 9.
    Zhou, W., Habetler, T.G., Harley, R.G. Stator current based bearing fault detection techniques: a general review. In: Proceedings of IEEE international Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 7–10. (2007)Google Scholar
  10. 10.
    Blodt, M., Granjon, P., Raison, B., Rostaing, G.: Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)CrossRefGoogle Scholar
  11. 11.
    Cusido, J., Rosero, J., Aldabas, E., Ortega, J.A., Romeral, L.: New fault detection techniques for induction motors. Electr. Power Qual. Util. Mag. 11(1), 39–45 (2006)Google Scholar
  12. 12.
    Antonio-Daviu, J.A., Riera-Guasp, M., Floch, J.R., Palomares, M.P.M.: Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines. IEEE Tran. Ind. Appl. 42(4), 990–996 (2006)CrossRefGoogle Scholar
  13. 13.
    Ordaz-Moreno, A., Romero-Troncoso, R.J., Vite-Frias, J.A., Rivera-Gillen, J.R., Garcia-Perez, A.: Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation. IEEE Trans. Ind. Electron. 55(5), 2193–2202 (2008)CrossRefGoogle Scholar
  14. 14.
    Caruso, G., Iannuzzi, D., Maceri, F., Pagano, E., Piegari, L. Torsional eigenfrequency identification of squirrel cage rotors of induction motors. In: Proceedings of International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 1271–1275 (2008)Google Scholar
  15. 15.
    Benbouzid, M.E.H.: A review of induction motors signature analysis as a medium for faults detection. IEEE Trans Ind Electron. 47(5), 984–993 (2000)CrossRefGoogle Scholar
  16. 16.
    Li, W.: Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods. J. Vib. Control 17(2), 165–188 (2006)CrossRefGoogle Scholar
  17. 17.
    Dash, R.N., Subudhi, B., Das, S.: Induction motor stator inter-turn fault detection using wavelet transform technique. In: Proceedings of International Conference on Industrial and Information Systems, pp. 436–441 (2010)Google Scholar
  18. 18.
    Mohammed, O.A., Abed, N.Y., Garni, S.C.: 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
  19. 19.
    Riera-Guasp, M., Antonino-Daviu, J., Roger-Folch, J., Molina, M.P.: The use of the wavelet approximation signal as a tool for the diagnosis of rotor bar failures. IEEE Trans. Ind. Appl. 44(3), 716–726 (2008)CrossRefGoogle Scholar

Copyright information

© ASM International 2013

Authors and Affiliations

  • Ridha Kechida
    • 1
  • Arezki Menacer
    • 2
  • Hicham Talhaoui
    • 3
  1. 1.LGEB Laboratory, BiskraDepartment of Electrical Engineering, University El-oued, AlgeriaBiskraAlgeria
  2. 2.LGEB Laboratory, Department of Electrical Engineering BiskraUniversity of Biskra, AlgeriaBiskraAlgeria
  3. 3.LGEB Laboratory, BiskraDepartment of Electromechanics, Institute of Sciences and Technology, University of Bordj Bou Arreridj, AlgeriaBiskraAlgeria

Personalised recommendations