Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 33–42Cite as

A Histogram of Oriented Gradients for Broken Bars Diagnosis in Squirrel Cage Induction Motors

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11139))

Abstract

The three-phase induction motors are widely used in a lot of applications both industry and other environments. Although this electrical machine is robust and reliable for industrial tasks, for example, conditioning monitoring techniques have been investigated during the last years to identify some electrical and mechanical faults in induction motors. In this sense, broken rotor bars is a typical fault related to the induction machine damage and the current technical solutions have shown some drawbacks for this kind of failure diagnosis, particularly when motor is running at very low slip. Therefore, this paper proposes a new use of Histogram of Oriented Gradients, usually applied in computer vision and image processing, for broken bars detection, using data from only one phase of the stator current of the machine. The intensity gradients and edge directions of each time-window of the stator signal have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5 kW squirrel cage induction machine running at distinct load levels (slip conditions).

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Zhang, P., Du, Y., Habetler, T.G., Lu, B.: A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47, 34–46 (2011)

    Article  Google Scholar 

  2. Bonnett, A.H., Soukup, G.C.: Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors. IEEE Trans. Ind. Appl. 28, 921–937 (1992)

    Article  Google Scholar 

  3. Thorsen, O.V., Dalva, M.: Failure identification and analysis for high-voltage induction motors in the petrochemical industry. IEEE Trans. Ind. Appl. 35, 810–818 (1999)

    Article  Google Scholar 

  4. Lee, S.B., et al.: Identification of false rotor fault indications produced by online MCSA for medium-voltage induction machines. IEEE Trans. Ind. Appl. 52, 729–739 (2016)

    Article  Google Scholar 

  5. Riera-Guasp, M., Cabanas, M.F., Antonino-Daviu, J.A., Pineda-Sánchez, M., García, C.H.R.: Influence of nonconsecutive bar breakages in motor current signature analysis for the diagnosis of rotor faults in induction motors. IEEE Trans. Energy Convers. 25, 80–89 (2010)

    Article  Google Scholar 

  6. Sizov, G.Y., Sayed-Ahmed, A., Yeh, C.-C., Demerdash, N.A.O.: Analysis and diagnostics of adjacent and nonadjacent broken-rotor-bar faults in squirrel-cage induction machines. IEEE Trans. Ind. Electron. 56, 4627–4641 (2009)

    Article  Google Scholar 

  7. Puche-Panadero, R., et al.: Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Trans. Energy Convers. 24, 52–59 (2009)

    Article  Google Scholar 

  8. Xu, B., Sun, L., Xu, L., Xu, G.: Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip. IEEE Trans. Energy Convers. 28, 225–233 (2013)

    Article  Google Scholar 

  9. Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Martinez-Roman, J., Kanović, Ž.: Low-cost diagnosis of rotor asymmetries in induction machines working at a very low slip using the reduced envelope of the stator current. IEEE Trans. Energy Convers. 30, 1409–1419 (2015)

    Article  Google Scholar 

  10. Valles-Novo, R., de Jesus Rangel-Magdaleno, J., Ramirez-Cortes, J.M., Peregrina-Barreto, H., Morales-Caporal, R.: Empirical mode decomposition analysis for broken-bar detection on squirrel cage induction motors. IEEE Trans. Instrum. Meas. 64, 1118–1128 (2015)

    Article  Google Scholar 

  11. Dias, C.G., Chabu, I.E.: Spectral analysis using a Hall effect sensor for diagnosing broken bars in large induction motors. IEEE Trans. Instrum. Meas. 63, 2890–2902 (2014)

    Article  Google Scholar 

  12. 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. Instrum. Meas. 58, 2253–2263 (2009)

    Article  Google Scholar 

  13. Singh, H., Seera, M., Abdullah, M.Z.: Detection and diagnosis of broken rotor bars and eccentricity faults in induction motors using the Fuzzy Min-Max neural network. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–5 (2013)

    Google Scholar 

  14. Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Landassuri-Moreno, V.M., de Jesús Medel-Juárez, J.: Misalignment identification in induction motors using orbital pattern analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 50–58. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41827-3_7

    Chapter  Google Scholar 

  15. Chandralekha, R., Jayanthi, D.: Diagnosis of Faults in Three Phase Induction Motor using Neuro Fuzzy Logic. Int. J. Appl. Eng. Res. 11, 5735–5740 (2016)

    Google Scholar 

  16. Lei, Y.: Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Butterworth-Heinemann (2016)

    Google Scholar 

  17. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  18. Yu, Y., Cao, H., Liu, S., Yang, S., Bai, R.: Image-based damage recognition of wind turbine blades. In: 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 161–166 (2017)

    Google Scholar 

  19. Meng, L., Wang, Z., Fujikawa, Y., Oyanagi, S.: Detecting cracks on a concrete surface using histogram of oriented gradients. In: 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 103–107 (2015)

    Google Scholar 

  20. Martin-Diaz, I., et al.: An experimental comparative evaluation of machine learning techniques for motor fault diagnosis under various operating conditions. IEEE Trans. Ind. Appl. 54(3), 2215–2224 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank UNINOVE and FAPESP - São Paulo Research Foundation (Process 2016/02547-5 and 2016/02525-1) by financial support.

Author information

Authors and Affiliations

  1. Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, SP, Brazil

    Luiz C. Silva, Cleber G. Dias & Wonder A. L. Alves

Authors
  1. Luiz C. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cleber G. Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wonder A. L. Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luiz C. Silva .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, L.C., Dias, C.G., Alves, W.A.L. (2018). A Histogram of Oriented Gradients for Broken Bars Diagnosis in Squirrel Cage Induction Motors. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01418-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation