Skip to main content
SpringerLink
Log in

Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

Fault detection in electrical machines is a topic widely explored by researchers, especially bearing faults that represent about half of the total three-phase induction motor failure occurrences. This kind of fault is detectable by specific frequencies of the stator current and is a wide source of investigation. Thus, this work presents a predictability analysis method that provides patterns based on measures of relative entropy, Bhattacharyya distance, and Lempel–Ziv complexity estimated over reconstructed signals obtained from wavelet packet decomposition components. The signals under study were collected from motors with faults in the inner or outer races, which were artificially created in laboratory. These patterns were applied to three neural network topologies, which were used to classify the signals into two groups: normal or faulty.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Addison, P. S. (2002). The illustrated wavelet transform handbook—Introductory theory and applications in science, engineering, medicine and finance. New York: Taylor and Francis.

    Book  MATH  Google Scholar 

  • Awadallah, M. A., & Morcos, M. M. (2003). Application of ai tools in fault diagnosis of electrical machines and drives—An overview. IEEE Transactions on Energy Conversion, 18(2), 245–251.

    Article  Google Scholar 

  • Bayindir, R., Sefa, I., Colak, I., & Bektas, A. (2008). Fault detection and protection of induction motors using sensors. IEEE Transactions on Energy Conversion, 23(3), 734–741.

    Article  Google Scholar 

  • Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G. A. (2008). Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics, 55(12), 4109–4126.

    Article  Google Scholar 

  • Benbouzid, M. E. H., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Transactions on Energy Conversion, 18(2), 238–244.

    Article  Google Scholar 

  • Bonnett, A., & Yung, C. (2008). Increased efficiency versus increased reliability. IEEE Industry Applications Magazine, 14(1), 29–36.

    Article  Google Scholar 

  • Broniera, P. J., Gongora, W. S., Goedtel, A., & Godoy, W. F. (2013). Diagnosis of stator winding inter-turn short circuit in three-phase induction motors by using artificial neural networks. In Proceedings of the 9th IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED), pp. 281–287.

  • Bruzzese, C. (2014). Diagnosis of eccentric rotor in synchronous machines by analysis of split-phase currentspart I: Theoretical analysis. IEEE Transactions on Industrial Electronics, 61(8), 4193–4205.

    Article  Google Scholar 

  • Climente-Alarcon, V., Antonino-Daviu, J. A., Riera-Guasp, M., & Vlcek, M. (2014). Induction motor diagnosis by advanced notch FIR filters and the Wigner-Ville distribution. IEEE Transactions on Industrial Electronics, 61(8), 4217–4227.

    Article  Google Scholar 

  • Devaney, M., & Eren, L. (2004). Detecting motor bearing faults. IEEE Instrumentation Measurement Magazine, 7(4), 30–50.

    Article  Google Scholar 

  • Diniz, P. S. R., Silva, E. A. B., & Netto, S. L. (2010). Digital signal processing: System analysis and design. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ebrahimi, B. M., Roshtkhari, M. J., Faiz, J., & Khatam, S. V. (2014). Advanced eccentricity fault recognition in permanent magnet synchronous motors using stator current signature analysis. IEEE Transactions on Industrial Electronics, 61(4), 2041–2052.

    Article  Google Scholar 

  • Eren, L., & Devaney, M. (2004). Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Transactions on Instrumentation and Measurement, 53(2), 431–436.

    Article  Google Scholar 

  • Feil, M., & Uhl, A. (1998). Wavelet packet decomposition and best basis selection on massively parallel SIMD arrays. In Proceedings of the international conference on wavelets and multiscale methods.

  • Godoy, W. F., Silva, I. N., Goedtel, A., & Palácios, R. H. C. (2015). Evaluation of stator winding faults severity in inverter-fed induction motors. Applied Soft Computing, 32, 420–431.

    Article  Google Scholar 

  • Gongora, W. S., Silva, H. V. D., Goedtel, A., Godoy, W. F., & da Silva, S. A. O. (2013). Neural approach for bearing fault detection in three phase induction motors. In Proceedings of the 9th IEEE international symposium on diagnostics for electric machines, Power electronics and drives (SDEMPED), pp. 566–572.

  • Guido, R. C. (2011). A note on a practical relationship between filter coefficients and scaling and wavelet functions of discrete wavelet transforms. Applied Mathematics Letters, 24, 1257–1259.

    Article  MathSciNet  Google Scholar 

  • Guido, R. C., Slaets, J. F. W., Koberle, R., Almeida, L. O. B., & Pereira, J. C. (2006). A new technique to construct a wavelet transform matching a specified signal with applications to digital, real time, spike, and overlap pattern recognition. Digital Signal Processing, 16(1), 24–44.

    Article  Google Scholar 

  • Haykin, S. (2008). Neural networks and learning machines (3rd ed.). New Jersey: Prentice Hall.

    Google Scholar 

  • Jin, X., Zhao, M., Chow, T. W. S., & Pecht, M. (2014). Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Transactions on Industrial Electronics, 61(5), 2441–2451.

    Article  Google Scholar 

  • Kailath, T. (1967). The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communication Technology, 15(1), 52–60.

    Article  Google Scholar 

  • Kankar, P., Sharma, S. C., & Harsha, S. (2011). Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing, 74(10), 1638–1645.

  • Kaspar, F., & Schuster, H. G. (1987). Easily calculable measure for the complexity of spatiotemporal patterns. Physical Review A, 36, 842–848.

  • Kazzaz, S. A. S. A., & Singh, G. (2003). Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques. Electric Power Systems Research, 65, 197–221.

    Article  Google Scholar 

  • Lamim, P. Filho, Pederiva, R., & Brito, J. N. (2014). Detection of stator winding faults in induction machines using flux and vibration analysis. Mechanical Systems and Signal Processing, 42, 377–387.

    Article  Google Scholar 

  • Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42, 314–334.

    Article  Google Scholar 

  • Lempel, A., & Ziv, J. (1976). On the complexity of finite sequences. IEEE Transactions on Information Theory, 22(1), 75–81.

    Article  MathSciNet  MATH  Google Scholar 

  • Palácios, R. H. C., da Silva, I. N., Goedtel, A., Godoy, W. F., & Oleskovicz, M. (2014). A robust neural method to estimate torque in three-phase induction motor. Journal of Control, Automation and Electrical Systems, 25(4), 493–502.

    Google Scholar 

  • Santos, T. H., Goedtel, A., Silva, S. A. O., & Suetake, M. (2014). Scalar control of an induction motor using a neural sensorless technique. Electric Power Systems Research, 108, 322–330.

    Article  Google Scholar 

  • Scalassara, P. R., Maciel, C. D., & Pereira, J. C. (2009a). Predictability analysis of voice signals. IEEE Engineering in Medicine and Biology Magazine, 28, 30–34.

    Article  Google Scholar 

  • Scalassara, P. R., Maciel, C. D., Pereira, J. C., Oliveira, S., & Stewart, D. (2009b). Problems with nonparametric entropy estimation of voice signals. In Proceedings of the 20th international congress on mechanical engineering, p. 1677.

  • Schmitt, H, Silva, L., Scalassara, P., & Goedtel, A. (2013). Bearing fault detection using relative entropy of wavelet components and artificial neural networks. In Proceedings of the 9th IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED), pp. 538–543.

  • Seera, M., Lim, C. P., Ishak, D., & Singh, H. (2013). Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model. Applied Soft Computing, 13(12), 4493–4507.

    Article  Google Scholar 

  • Seiffert, U., & Jain, L. C. (Eds.). (2002). Self-organizing neural networks: Recent advances and applications. New York: Physica-Verlag HD.

  • Suetake, M., da Silva, I. N., & Goedtel, A. (2011). Embedded DSP-based compact fuzzy system and its application for induction-motor v/f speed control. IEEE Transactions on Industrial Electronics, 58(3), 750–760.

    Article  Google Scholar 

  • Ukil, A., Chen, S., & Andenna, A. (2011). Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants. Electric Power Systems Research, 81, 1036–1044.

    Article  Google Scholar 

  • Yan, R., & Gao, R. (2004). Complexity as a measure for machine health evaluation. IEEE Transactions on Instrumentation and Measurement, 53(4), 1327–1334.

    Article  MathSciNet  Google Scholar 

  • Zhang, P., Du, Y., Habetler, T., & Lu, B. (2011). A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Transactions on Industry Applications, 47(1), 34–46.

    Article  Google Scholar 

  • Zhou, W., Habetler, T., & Harley, R. (2007). Stator current-based bearing fault detection techniques: A general review. In IEEE international symposium on diagnostics for electric machines, power electronics and drives (SDEMPED), pp. 7–10.

Download references

Acknowledgments

The authors thank the National Council for the Scientific and Technological Development (CNPq) processes 474290/2008-5, 473576/2011-2, and 552269/2011-5, and also the Araucária Foundation of Support for the Paraná Scientific and Technological Development processes 338/2012 and 06/56098-3.

Author information

Authors and Affiliations

  1. Federal University of Technology - Paraná, Cornélio Procópio, Brazil

    Helder Luiz Schmitt, Paulo Rogério Scalassara, Alessandro Goedtel & Wagner Endo

Authors
  1. Helder Luiz Schmitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Rogério Scalassara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Goedtel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wagner Endo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo Rogério Scalassara.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schmitt, H.L., Scalassara, P.R., Goedtel, A. et al. Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks. J Control Autom Electr Syst 26, 535–544 (2015). https://doi.org/10.1007/s40313-015-0203-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-015-0203-5

Keywords

Search

Navigation