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Filter Banks as Proposal in Electrical Motors Fault Discrimination

  • Jhonattan Bulla
  • Alvaro David Orjuela-CañónEmail author
  • Oscar D. Flórez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 833)

Abstract

Studies related with the induction motor bearings fault detection have been used digital signal processing and pattern recognition techniques. However, performance of these approaches depends on the use of correct features. This paper deals an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Discrimination was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.

Keywords

Induction motor Bearing faults Filter bank Artificial neural networks Feature extraction 

Notes

Acknowledgment

Authors thank the Universidad Antonio Nariño under project 2017211 and code PI/UAN-2018-628GIBIO. Also, Universidad Distrital Francisco Jose de Caldas contributed with the support and financial assistance in this work.

References

  1. 1.
    Randall, R.B., Antoni, J.: Rolling element bearing diagnostics-a tutorial. Mech. Syst. Sig. Process. 25, 485–520 (2011)CrossRefGoogle Scholar
  2. 2.
    Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech. Syst. Sig. Process. 64, 100–131 (2015)CrossRefGoogle Scholar
  3. 3.
    El-Thalji, I., Jantunen, E.: A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech. Syst. Sig. Process. 60, 252–272 (2015)CrossRefGoogle Scholar
  4. 4.
    Caesarendra, W., Tjahjowidodo, T.: A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5, 21 (2017)CrossRefGoogle Scholar
  5. 5.
    Wang, H., Chen, P.: A feature extraction method based on information theory for fault diagnosis of reciprocating machinery. Sensors 9, 2415–2436 (2009)CrossRefGoogle Scholar
  6. 6.
    Chebil, J., Hrairi, M., Abushikhah, N.: Signal analysis of vibration measurements for condition monitoring of bearings. Aust. J. Basic Appl. Sci. 5, 70–78 (2011)Google Scholar
  7. 7.
    Rai, A., Upadhyay, S.H.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 96, 289–306 (2016)CrossRefGoogle Scholar
  8. 8.
    Liu, J., Wang, W., Golnaraghi, F., Liu, K.: Wavelet spectrum analysis for bearing fault diagnostics. Meas. Sci. Technol. 19, 15105 (2007)CrossRefGoogle Scholar
  9. 9.
    Porat, B.: A Course in Digital Signal Processing. Wiley, New York (1997)Google Scholar
  10. 10.
    Strang, G., Nguyen, T.: Wavelets and Filter Banks. SIAM, Philadelphia (1996)zbMATHGoogle Scholar
  11. 11.
    Rafiee, J., Rafiee, M.A., Tse, P.W.: Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst. Appl. 37, 4568–4579 (2010)CrossRefGoogle Scholar
  12. 12.
    Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Sig. Process. 18, 1077–1095 (2004)CrossRefGoogle Scholar
  13. 13.
    Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Appl. Soft Comput. 11, 4203–4211 (2011)CrossRefGoogle Scholar
  14. 14.
    Garcia-Perez, A., de Jesus Romero-Troncoso, R., Cabal-Yepez, E., Osornio-Rios, R.A.: The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans. Ind. Electron. 58, 2002–2010 (2011)CrossRefGoogle Scholar
  15. 15.
    Zarei, J.: Induction motors bearing fault detection using pattern recognition techniques. Expert Syst. Appl. 39, 68–73 (2012)CrossRefGoogle Scholar
  16. 16.
    Ghate, V.N., Dudul, S.V.: Optimal MLP neural network classifier for fault detection of three phase induction motor. Expert Syst. Appl. 37, 3468–3481 (2010)CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Xiong, G., Liu, H., Zou, H., Guo, W.: Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 37, 6077–6085 (2010)CrossRefGoogle Scholar
  18. 18.
    Kia, S.H., Henao, H., Capolino, G.-A.: Some digital signal processing techniques for induction machines diagnosis. In: 2011 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED), pp. 322–329 (2011)Google Scholar
  19. 19.
    Loparo, K.A.: Case Western Reserve University Bearing Data Center (2012)Google Scholar
  20. 20.
    Wang, D., Peter, W.T., Tsui, K.L.: An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Sig. Process. 35, 176–199 (2013)CrossRefGoogle Scholar
  21. 21.
    Yiakopoulos, C.T., Gryllias, K.C., Antoniadis, I.A.: Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Expert Syst. Appl. 38, 2888–2911 (2011)CrossRefGoogle Scholar
  22. 22.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, Englewood Cliffs (2009)Google Scholar
  23. 23.
    Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991)CrossRefGoogle Scholar
  24. 24.
    Sheela, K.G., Deepa, S.N.: Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. 2013, 7–9 (2013)CrossRefGoogle Scholar
  25. 25.
    Bishop, C.: Pattern Recognition and Machine Learning. Information Science and Statistics, 1st edn. Springer, New York (2007). Corr. 2nd printing edn. Springer, New York (2007)zbMATHGoogle Scholar
  26. 26.
    Zaeri, R., Ghanbarzadeh, A., Attaran, B., Moradi, S.: Artificial neural network based fault diagnostics of rolling element bearings using continuous wavelet transform. In: 2011 2nd International Conference on Control, Instrumentation and Automation (ICCIA), pp. 753–758 (2011)Google Scholar
  27. 27.
    Prieto, M.D., Cirrincione, G., Espinosa, A.G., Ortega, J.A., Henao, H.: Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 60, 3398–3407 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Universidad Antonio NariñoBogotá D.C.Colombia
  2. 2.Universidad Distrital Francisco José de CaldasBogotáColombia

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