Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 3409756, 7 pages
http://dx.doi.org/10.1155/2016/3409756
Research Article

Stator Fault Detection in Induction Motors by Autoregressive Modeling

1Departamento de Ingeniería Electronica, Instituto Tecnologico de Aguascalientes, Avenida Adolfo Lopez Mateos No. 1801 Oriente, Aguascalientes, AGS, Mexico
2CIEP, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Avenida Dr. Manuel Nava No. 8, San Luis Potosí, SLP, Mexico
3Departamento de Estudios Multidisciplinarios, Division de Ingenierías Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, 38944 Yuriria, GTO, Mexico

Received 24 November 2015; Revised 2 March 2016; Accepted 8 March 2016

Academic Editor: Peter Dabnichki

Copyright © 2016 Francisco M. Garcia-Guevara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. A. Toliyat, S. Nandi, S. Choi, and H. Meshging-Kelk, Electric Machines, CRC Press, Boca Raton, Fla, USA, 2013.
  2. H. Henao, G.-A. Capolino, M. Fernandez-Cabanas et al., “Trends in fault diagnosis for electrical machines: a review of diagnostic techniques,” IEEE Industrial Electronics Magazine, vol. 8, no. 2, pp. 31–42, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors—a review,” IEEE Transactions on Energy Conversion, vol. 20, no. 4, pp. 719–729, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. F. Betin, G.-A. Capolino, D. Casadei et al., “Trends in electrical machines control: samples for classical, sensorless, and fault-tolerant techniques,” IEEE Industrial Electronics Magazine, vol. 8, no. 2, pp. 43–55, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. C. H. De Angelo, G. R. Bossio, S. J. Giaccone, M. I. Valla, J. A. Solsona, and G. O. García, “Online model-based stator-fault detection and identification in induction motors,” IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4671–4680, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. S. M. A. Cruz and A. J. M. Cardoso, “Multiple reference frames theory: a new method for the diagnosis of stator faults in three-phase induction motors,” IEEE Transactions on Energy Conversion, vol. 20, no. 3, pp. 611–619, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Yang, “Automatic condition monitoring of industrial rolling-element bearings using motor's vibration and current analysis,” Shock and Vibration, vol. 2015, Article ID 486159, 12 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. H.-Y. Zhu, J.-T. Hu, L. Gao, and H. Huang, “Practical aspects of broken rotor bars detection in PWM voltage-source-inverter-fed squirrel-cage induction motors,” Journal of Applied Mathematics, vol. 2013, Article ID 128368, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Shnibha, A. Albarbar, A. Abouhnik, and G. Ibrahim, “A more reliable method for monitoring the condition of three-phase induction motors based on their vibrations,” ISRN Mechanical Engineering, vol. 2012, Article ID 230314, 9 pages, 2012. View at Publisher · View at Google Scholar
  10. A. H. Bonnett and C. Yung, “Increased efficiency versus increased reliability,” IEEE Industry Applications Magazine, vol. 14, no. 1, pp. 29–36, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Hernandez-Vargas, E. Cabal-Yepez, and A. Garcia-Perez, “Real-time SVD-based detection of multiple combined faults in induction motors,” Computers & Electrical Engineering, vol. 40, no. 7, pp. 2193–2203, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Zhang, Y. Du, T. G. Habetler, and B. Lu, “A survey of condition monitoring and protection methods for medium-voltage induction motors,” IEEE Transactions on Industry Applications, vol. 47, no. 1, pp. 34–46, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. R. N. Bell, D. W. McWilliams, P. O'Donnell, C. Singh, and S. J. Wells, “Report of large motor reliability survey of industrial and commercial installations, part I,” IEEE Transactions on Industry Applications, vol. IA-21, no. 4, pp. 853–864, 1985. View at Publisher · View at Google Scholar · View at Scopus
  14. “Report of large motor reliability survey of industrial and commercial installations, Part II,” IEEE Transactions on Industry Applications, vol. 21, no. 4, pp. 865–872, 1985. View at Publisher · View at Google Scholar
  15. “Report of large motor reliability survey of industrial and commercial installations: part 3,” IEEE Transactions on Industry Applications, vol. 21, no. 4, pp. 865–872, 1987. View at Publisher · View at Google Scholar
  16. P. F. Albrecht, J. C. Appiarius, R. M. McCoy, E. L. Owen, and D. K. Sharma, “Assessment of the reliability of motors in utility applications—updated,” IEEE Transactions on Energy Conversion, vol. 1, no. 1, pp. 39–46, 1986. View at Publisher · View at Google Scholar
  17. H. O. Seinsch, “Monitoring und diagnose elektrischer maschinen und antriebe,” in Proceedings VDE Workshop, Allianz Schadensstatistik an HS Motoren, 1996–1999, VDE, 2001. View at Google Scholar
  18. L.-L. Jiang, H.-K. Yin, X.-J. Li, and S.-W. Tang, “Fault diagnosis of rotating machinery based on multisensor information fusion using SVM and time-domain features,” Shock and Vibration, vol. 2014, Article ID 418178, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. P. A. Delgado-Arredondo, A. Garcia-Perez, D. Morinigo-Sotelo et al., “Comparative study of time-frequency decomposition techniques for fault detection in induction motors using vibration analysis during startup transient,” Shock and Vibration, vol. 2015, Article ID 708034, 14 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Morton, P. A. Torrione, and L. M. Collins, “Variational Bayesian learning for mixture autoregressive models with uncertain-order,” IEEE Transactions on Signal Processing, vol. 59, no. 6, pp. 2614–2627, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. G. Wang, Z. Luo, X. Qin, Y. Leng, and T. Wang, “Fault identification and classification of rolling element bearing based on time-varying autoregressive spectrum,” Mechanical Systems and Signal Processing, vol. 22, no. 4, pp. 934–947, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Liu, X. Zhou, S. Yang, W. Liang, and Q. Miao, “Cooling fan bearing diagnosis based on AR& MED,” in Proceedings of the International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE '12), pp. 622–626, Chengdu, China, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. G.-H. Zhou, C.-C. Zuo, J.-Z. Wang, and S.-X. Liu, “Gearbox fault diagnosis based on wavelet-AR model,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), vol. 2, pp. 1061–1065, Hong Kong, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. N. K. Nikhar, S. S. Patankar, and J. V. Kulkarni, “Gear tooth fault detection by autoregressive modelling,” in Proceedings of the International Conference on Computing, Communications and Networking Technologies (ICCCNT '13), pp. 1–6, Tiruchengode, India, July 2013.
  25. F. J. Villalobos-Piña and R. Alvarez-Salas, “Robust algorithm for electric fault diagnosis in the three phase induction machine based on spectral and wavelet tools,” Revista Iberoamericana de Automatica e Informatica Industrial, vol. 12, no. 3, pp. 292–303, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. D. L. Milanez and A. E. Emanuel, “The instantaneous-space-phasor: a powerful diagnosis tool,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 1, pp. 143–148, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Lin, Y. Xie, D. Zhao, and S. Xu, “Estimation of observer parameters for dynamic positioning ships,” Mathematical Problems in Engineering, vol. 2013, Article ID 173603, 7 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  28. J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Pearson Prentice Hall, New Jersey, NJ, USA, 2007.
  29. M. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York, NY, USA, 1996.
  30. A. Neumaier and T. Schneider, “Estimation of parameters and eigenmodes of multivariate autoregressive models,” ACM Transactions on Mathematical Software, vol. 27, no. 1, pp. 27–57, 2001. View at Publisher · View at Google Scholar · View at Scopus
  31. H.-C. Wu, S. Y. Chang, T. Le-Ngoc, and Y. Wu, “Efficient rank-adaptive least-square estimation and multiple-parameter linear regression using novel dyadically recursive hermitian matrix inversion,” International Journal of Antennas and Propagation, vol. 2012, Article ID 891932, 10 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Drif and A. J. M. Cardoso, “Airgap-eccentricity fault diagnosis, in three-phase induction motors, by the complex apparent power signature analysis,” IEEE Transactions on Industrial Electronics, vol. 55, no. 3, pp. 1404–1410, 2008. View at Publisher · View at Google Scholar · View at Scopus