e & i Elektrotechnik und Informationstechnik

, Volume 135, Issue 2, pp 187–194 | Cite as

Experimental investigation on induction motors inter-turns short-circuit and broken rotor bars faults diagnosis through the discrete wavelet transform

  • Fateh Benchabane
  • Abderezak Guettaf
  • Khaled Yahia
  • Mohamed Sahraoui
Originalarbeit
  • 23 Downloads

Abstract

This paper deals with the problem of fault detection in induction motors using the discrete wavelet transform (DWT) method. The DWT is a mathematical method used to extract different frequency components from a given signal. It is based on the decomposition of the processed signals into wavelet approximation and detail coefficients. In order to detect inter-turns short-circuit (ITSC) and broken rotor bars (BRBs) faults, the DWT is applied on two different signals: the current envelope and the current Park’s vector modulus. This study is performed using experimental tests carried-out on a 3 kW squirrel cage induction motor. The energy evaluation of known bandwidth details allows defining a fault severity factor (FSF). This FSF is used to show which signals, wavelet type and wavelet order are more sensitive for the fault detection task.

Keywords

induction motors discrete wavelet transform inter-turns short-circuit broken rotor bars current envelope current Park’s vector modulus 

Experimentelle Untersuchung von Windungsschlüssen und Stabbrüchen an Asynchronmaschinen mittels diskreter Wavelet-Transformation

Zusammenfassung

Diese Arbeit befasst sich mit der Fehlererkennung in Asynchronmaschinen mittels diskreter Wavelet-Transformation (DWT). Die DWT ist eine mathematische Methode, um verschiedene Frequenzkomponenten aus einem gegebenen Signal zu extrahieren. Sie basiert auf der Zerlegung des verarbeiteten Signals in Tieffrequenzanteile und Hochfrequenzanteile. Um Windungsschlüsse und Rotorstabbrüche zu erkennen, wird die DWT auf zwei unterschiedliche Signale angewendet: die Hüllkurve des Stroms und den Betrag des Stromvektors nach der Park-Transformation. Die Untersuchungen wurden basierend auf Messungen an einer 3-kW-Asynchronmaschine mit Kurzschlussläufer durchgeführt. Die Auswertung der Leistungsanteile über den Frequenzbereich erlaubt die Definition eines Fehlerlevel-Faktors. Dieser Faktor kann verwendet werden, um für die Fehlererkennung geeignete Signale, Wavelet-Typen und Wavelet-Ordnungszahlen zu ermitteln.

Schlüsselwörter

Asynchronmaschine diskrete Wavelet-Transformation Windungsschluss Rotorstabbruch Strom-Hüllkurve Park-Transformation des Stroms 

References

  1. 1.
    Penman, J., Sedding, H. G., Lloyd, B. A., Fink, W. T. (1994): Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Trans. Energy Convers., 9(4), 652–658. CrossRefGoogle Scholar
  2. 2.
    Tallam, R. M., Habetler, T. G., Harley, R. G., Gritter, D. J., Burton, B. H. (2000): Neural network based on-line stator winding turn fault detection for induction motors. In Proc. IEEE-IAS Conf. (pp. 375–380). Google Scholar
  3. 3.
    Kallesøe, C. S., Zamanabadi, R. I., Vadstrup, P., Rasmussen, H. (2007): Observer-based estimation of stator-winding faults in delta-connected induction motors: a linear matrix inequality approach. IEEE Trans. Ind. Appl., 43(4), 1022–1031. CrossRefGoogle Scholar
  4. 4.
    Cardoso, A. J. M., Cruz, S. M. A., Fonseca, D. S. B. (1999): Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park’s vector approach. IEEE Trans. Energy Convers., 14, 595–598. CrossRefGoogle Scholar
  5. 5.
    Cruz, S. M. A., Cardoso, A. J. M. (2001): Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach. IEEE Trans. Ind. Appl., 37, 1227–1233. CrossRefGoogle Scholar
  6. 6.
    Cruz, S. M. A., Cardoso, A. J. M. (2005): Multiple reference frames theory: a new method for the diagnosis of stator faults in three-phase induction motors. IEEE Trans. Energy Convers., 20(3), 611–617. CrossRefGoogle Scholar
  7. 7.
    Briz, F., Degner, M. W., García, P., Diez, A. B. (2008): High-frequency carrier-signal voltage selection for stator winding fault diagnosis in inverter-fed AC machines. IEEE Trans. Ind. Electron., 55(12), 1109–1117. CrossRefGoogle Scholar
  8. 8.
    Bachir, S., Tnani, S., Trigeassou, J. C., Champenois, G. (2006): Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Trans. Ind. Electron., 53(3), 345–352. CrossRefGoogle Scholar
  9. 9.
    Ferdjouni, A., Salhi, H., Djemai, M., Busawon, K. (2006): Observer-based detection of inter-turn short circuit in three phase induction motor stator windings. Mediterr. J. Meas. Control, 2(3), 132–143. Google Scholar
  10. 10.
    Kliman, G. B., Stein, J. (1992): Methods of motor currents signature analysis. Electr. Mach. Power Syst., 20(5), 463–474. CrossRefGoogle Scholar
  11. 11.
    Thomson, W. T., Fenger, M. (2001): Current signature analysis to detect induction motors faults. IEEE Ind. Appl. Mag., 7(4), 26–34. CrossRefGoogle Scholar
  12. 12.
    Maouche, Y., Oumaamar, M. K., Boucherma, M., Khezzar, A. (2014): Instantaneous power spectrum analysis for broken bar fault detection in inverter-fed six-phase squirrel cage induction motor. Int. J. Electr. Power Energy Syst., 62(4), 110–117. CrossRefGoogle Scholar
  13. 13.
    Magdaleno, J. R., Barreto, H. P., Cortes, J. R., Vega, I. C. (2017): Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars. Measurement, 109(3), 247–255. CrossRefGoogle Scholar
  14. 14.
    Cruz, S. M. A., Cardoso, A. J. M. (2000): Rotor cage fault diagnosis in three-phase induction motors by the extended Park’s vector approach. Electr. Mach. Power Syst., 28(4), 289–299. CrossRefGoogle Scholar
  15. 15.
    Hsu, J. S. (1995): Monitoring of defects in induction motors through air-gap torque observation. IEEE Trans. Ind. Appl., 31(5), 1016–1021. CrossRefGoogle Scholar
  16. 16.
    Eltabach, M., Charara, A. (2007): Comparative investigation of electric signal analyses methods for mechanical fault detection in induction motors. Electr. Power Compon. Syst., 35(10), 1161–1180. CrossRefGoogle Scholar
  17. 17.
    Liu, Z., Yin, X., Zhang, Z., Chen, D. (2004): Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors. IEEE Trans. Energy Convers., 19(3), 485–490. CrossRefGoogle Scholar
  18. 18.
    Drif, M., Cardoso, A. J. M. (2008): The use of the instantaneous-reactive-power signature analysis for rotor-cage-fault diagnostics in three-phase induction motors. IEEE Trans. Ind. Electron., 56(11), 4606–4614. CrossRefGoogle Scholar
  19. 19.
    Wang, Y., He, Z., Zi, Y. (2010): Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech. Syst. Signal Process., 24(1), 119–137. CrossRefGoogle Scholar
  20. 20.
    Yahia, K., Cardoso, A. J. M., Ghoggal, A., Zouzou, S. E. (2014): Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions. ISA Trans., 53(2), 603–611. CrossRefGoogle Scholar
  21. 21.
    Mallat, S. G. (1998): A wavelet tour of signal processing. 2nd ed. San Diego: Academic Press. MATHGoogle Scholar
  22. 22.
    Malat, S. G. (1989): A Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 2(7), 674–693. CrossRefGoogle Scholar
  23. 23.
    Daviu, J. A., Rodriguez, P. J., Guasp, M. R., Sanchez, M. P., Arkkio, A. (2009): Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current. Mech. Syst. Signal Process., 23, 2336–2351. CrossRefGoogle Scholar
  24. 24.
    Awadallah, M. A., Morcos, M. M., Gopalakrishnan, S., Nehl, T. W. (2006): Detection of stator short circuits in VSI-fed brushless DC motors using wavelet transform. IEEE Trans. Energy Convers., 21(1) 1–8. CrossRefGoogle Scholar
  25. 25.
    Kia, S. H., Henao, H., Capolino, G. A. (2009): Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation. IEEE Trans. Ind. Appl., 45(4), 107–121. Google Scholar
  26. 26.
    Yahia, K., Cardoso, A. J. M., Ghoggal, A., Zouzou, S. (2014): Induction motors broken rotor bars diagnosis through the discrete wavelet transform of the instantaneous reactive power signal under time varying load conditions. Electr. Power Compon. Syst., 42(74), 682–692. CrossRefGoogle Scholar
  27. 27.
    Sahraoui, M., Ghoggal, A., Zouzou, S. E., Aboubou, A., Razik, H. (2006): Modelling and detection of inter-turn short circuits in stator windings of induction motor. In 32nd Annual Conference on IEEE Indus. Electron., IECON, Paris, France, 6–10 Nov. 2006 (pp. 4981–4986). Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, ein Teil von Springer Nature 2018

Authors and Affiliations

  • Fateh Benchabane
    • 1
  • Abderezak Guettaf
    • 1
  • Khaled Yahia
    • 2
  • Mohamed Sahraoui
    • 3
  1. 1.Laboratoire de Modélisation des Systèmes Energétiques, LMSEUniversité Mohamed Khider de BiskraBiskraAlgérie
  2. 2.Laboratoire de Génie Energétique et Matériaux, LGEMUniversité Mohamed Khider de BiskraBiskraAlgérie
  3. 3.Laboratoire de Génie Electrique de Biskra, LGEBUniversité Mohamed Khider de BiskraBiskraAlgérie

Personalised recommendations