Skip to main content
SpringerLink
Log in

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction electrical motor by applying the support vector machine. Two solutions of diagnostic system have been elaborated. The first one, called fault detection, discovers only the case of the fault occurrence. The second one (complex diagnosis) is able to find which bars have been damaged. The most important problem is concerned with the generation and selection of the diagnostic features, on the basis of which the recognition of the state of the rotor bars is done. In our approach, we use the spectral information of the motor current, voltage and shaft field of one phase registered in an instantaneous form. The selected features form the input vector applied to the support vector machine, used as the classifier. The results of the numerical experiments are presented and discussed in the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Aydin I, Karaköse M, Akin E (2007) Artificial immune based support vector machine algorithm for fault diagnosis of induction motors. IEEE Electr Mach Syst Conf (ICEMS) 2:1–5

    Google Scholar 

  2. Duda RO, Hart PE, Stork P (2003) Pattern classification and scene analysis. Wiley, New York

    Google Scholar 

  3. Fang R (2006) Induction machine rotor diagnosis using support vector machines and rough set. Lect Notes Comput Sci 4114:631–636

    Article  Google Scholar 

  4. Fenger M, Susnik M, Laderoute P, Thomson W (2003) Development of a tool to detect faults in induction motors via current signature analysis. Iris Rot Mach Conf, Santa Monica, pp 1–8

  5. Filippetti F, Francheschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47:994–1004

    Article  Google Scholar 

  6. Haji M, Toliyat H (2001) Pattern recognition—a technique for induction machine rotor fault detection “Broken bar fault”. IEEE IAS Trans 37:899–904

    Google Scholar 

  7. Hubert C (2002) Electric machines: theory, operating applications, and controls. Prentice Hall, Englewood Cliffs

    Google Scholar 

  8. Kliman GB, Stein J (1990) Induction motor fault detection via passive current monitoring. In: Proceedings of international conference (ICEM’90), MIT, Boston, USA, pp 13–17

  9. Li L, Mechefske CK, Li W (2004) Electric motor faults diagnosis using artificial neural networks. Insight—Non-Destruct Test Cond Monit 46:616–621

    Article  Google Scholar 

  10. Kurek J, Biernat A, Osowski S, Markiewicz T (2006) Diagnosis of induction motor using support vector machine, VII Conference CPEE, Odessa, Ukraine, 98–101

  11. Matlab user manual (2002) MathWorks, Natick, USA

  12. Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55:169–186

    Article  Google Scholar 

  13. Mirafzal B, Demerdash N (2004) Induction machine broken-bar fault diagnosis using the rotor magnetic field space vector orientation. IEEE Trans Ind Appl 40:534–542

    Article  Google Scholar 

  14. Penman J, Sedding HG, Lloyd BA, Fink WT (1994) Detection and location of inertium short circuits in the stator winding of operating motors. IEEE Trans Energy Conserv 9:694–699

    Google Scholar 

  15. Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge

    Google Scholar 

  16. Schoen R, Lin BK, Habetler FG, Schlog HJ, Farag S (1995) An unsupervised on-line system for induction motor fault detection using stator current monitoring. IEEE IAS Trans 31:1280–1286

    Google Scholar 

  17. Su H, Chong KT, Parlos AG (2005) A neural network method for induction machine fault detection with vibration signal. Lect Notes Comput Sci Springer, Berlin, vol 3481, pp 1293–1302

  18. Supangat R, Ertugrul N, Soong WL, Gray DA, Hansen CH, Grieger J (2005) Broken rotor bar fault detection in induction motors using starting current analysis. In: Proceedings of 11th European conference on power electronics and applications, Dresden, Germany, pp 1–10

  19. Szabo L, Dobai JB, Biro KA (2004) Rotor faults detection in squirrel-cage induction motors by current signature analysis, 2004 IEEE-TTTC—international conference on automation, quality and testing, robotics, Cluj-Napoca, Romania, pp 1–4

  20. Weisberg S (2005) Applied linear regression. Wiley, New York

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Stanislaw Osowski

  2. Military University of Technology, Warsaw, Poland

    Stanislaw Osowski

  3. University of Life Sciences, Warsaw, Poland

    Jaroslaw Kurek

Authors
  1. Jaroslaw Kurek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stanislaw Osowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stanislaw Osowski.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kurek, J., Osowski, S. Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Comput & Applic 19, 557–564 (2010). https://doi.org/10.1007/s00521-009-0316-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0316-5

Keywords

Search

Navigation