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Shock and Vibration
Volume 2016, Article ID 4860309, 10 pages
http://dx.doi.org/10.1155/2016/4860309
Research Article

Shannon Entropy and -Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

1División de Ingenierías, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago Km 3.5 + 1.8, Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico
2Facultad de Ingeniería, Universidad Autónoma de Querétaro, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, 76807 San Juan del Río, QRO, Mexico
3Departamento de Ingeniería Electromecánica, Instituto Tecnológico Superior de Irapuato, Carretera Irapuato-Silao Km 12.5, Colonia El Copal, 36821 Irapuato, GTO, Mexico

Received 2 June 2016; Revised 1 August 2016; Accepted 21 August 2016

Academic Editor: Lu Chen

Copyright © 2016 David Camarena-Martinez 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. 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
  2. M. Hernandez-Vargas, E. Cabal-Yepez, and A. Garcia-Perez, “Real-time SVD-based detection of multiple combined faults in induction motors,” Computers and Electrical Engineering, vol. 40, no. 7, pp. 2193–2203, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. R. J. Romero-Troncoso, A. Garcia-Perez, D. Morinigo-Sotelo, O. Duque-Perez, R. A. Osornio-Rios, and M. A. Ibarra-Manzano, “Rotor unbalance and broken rotor bar detection in inverter-fed induction motors at start-up and steady-state regimes by high-resolution spectral analysis,” Electric Power Systems Research, vol. 133, pp. 142–148, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. J. A. Antonino-Daviu, M. Riera-Guasp, M. Pineda-Sanchez, and R. B. Pérez, “A critical comparison between DWT and Hilbert-Huang-based methods for the diagnosis of rotor bar failures in induction machines,” IEEE Transactions on Industry Applications, vol. 45, no. 5, pp. 1794–1803, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Valles-Novo, J. d. j. Rangel-Magdaleno, J. M. Ramirez-Cortes, H. Peregrina-Barreto, and R. Morales-Caporal, “Empirical mode decomposition analysis for broken-bar detection on squirrel cage induction motors,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 5, pp. 1118–1128, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y.-H. Kim, Y.-W. Youn, D.-H. Hwang, J.-H. Sun, and D.-S. Kang, “High-resolution parameter estimation method to identify broken rotor bar faults in induction motors,” IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 4103–4117, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Kurek and S. Osowski, “Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor,” Neural Computing and Applications, vol. 19, no. 4, pp. 557–564, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. S.-R. Huang, K.-H. Huang, K.-H. Chao, and W.-T. Chiang, “Fault analysis and diagnosis system for induction motors,” Computers & Electrical Engineering, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Rolling element bearing fault diagnosis using wavelet transform,” Neurocomputing, vol. 74, no. 10, pp. 1638–1645, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Bellini, A. Yazidi, F. Filippetti, C. Rossi, and G.-A. Capolino, “High frequency resolution techniques for rotor fault detection of induction machines,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4200–4209, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Garcia-Perez, J. P. Amezquita-Sanchez, A. Dominguez-Gonzalez, R. Sedaghati, R. Osornio-Rios, and R. J. Romero-Troncoso, “Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis,” Journal of Zhejiang University: Science A, vol. 14, no. 9, pp. 615–630, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Barakat, M. El Badaoui, and F. Guillet, “Hard competitive growing neural network for the diagnosis of small bearing faults,” Mechanical Systems and Signal Processing, vol. 37, no. 1-2, pp. 276–292, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Liu, H. Cao, X. Chen, Z. He, and Z. Shen, “Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings,” Neurocomputing, vol. 99, pp. 399–410, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramirez-Cortes, R. Morales-Caporal, and I. Cruz-Vega, “Vibration analysis of partially damaged rotor bar in induction motor under different load condition using DWT,” Shock and Vibration, vol. 2016, Article ID 3530464, 11 pages, 2016. View at Publisher · View at Google Scholar
  15. L. Zhang, L. Zhang, J. Hu, and G. Xiong, “Bearing fault diagnosis using a novel classifier ensemble based on lifting wavelet packet transforms and sample entropy,” Shock and Vibration, vol. 2016, Article ID 4805383, 13 pages, 2016. View at Publisher · View at Google Scholar
  16. R. Valles-Novo, J. D. J. Rangel-Magdaleno, J. M. Ramirez-Cortes, H. Peregrina-Barreto, and R. Morales-Caporal, “Empirical mode decomposition analysis for broken-bar detection on squirrel cage induction motors,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 5, pp. 1118–1128, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Climente-Alarcon, J. A. Antonino-Daviu, M. Riera-Guasp, and M. Vlcek, “Induction motor diagnosis by advanced notch FIR filters and the wigner-ville distribution,” IEEE Transactions on Industrial Electronics, vol. 61, no. 8, pp. 4217–4227, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. A. L. Martinez-Herrera, L. M. Ledesma-Carrillo, M. Lopez-Ramirez, S. Salazar-Colores, E. Cabal-Yepez, and A. Garcia-Perez, “Gabor and the Wigner-Ville transforms for broken rotor bars detection in induction motors,” in Proceedings of the 24th International Conference on Electronics, Communications and Computers (CONIELECOMP '14), pp. 83–87, IEEE, Puebla, Mexico, February 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Pan, T. Han, A. C. C. Tan, and T. R. Lin, “Fault diagnosis system of induction motors based on multiscale entropy and support vector machine with mutual information algorithm,” Shock and Vibration, vol. 2016, Article ID 5836717, 12 pages, 2016. View at Publisher · View at Google Scholar
  20. N. Saravanan and K. I. Ramachandran, “Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN),” Expert Systems with Applications, vol. 37, no. 6, pp. 4168–4181, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. G. F. Bin, J. J. Gao, X. J. Li, and B. S. Dhillon, “Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network,” Mechanical Systems and Signal Processing, vol. 27, no. 1, pp. 696–711, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. R. J. Romero-Troncoso, R. Saucedo-Gallaga, E. Cabal-Yepez et al., “FPGA-based online detection of multiple combined faults in induction motors through information entropy and fuzzy inference,” IEEE Transactions on Industrial Electronics, vol. 58, no. 11, pp. 5263–5270, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. P. Winston and M. Saravanan, “Single parameter fault identification technique for DC motor through wavelet analysis and fuzzy logic,” Journal of Electrical Engineering and Technology, vol. 8, no. 5, pp. 1049–1055, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Sharma and P. Kaur, “Detection and extraction of brain tumor from MRI images using K-means clustering and watershed algorithms,” International Journal of Computer Science Trends and Technology, vol. 3, no. 2, pp. 32–38, 2015. View at Google Scholar
  25. H. Qarib and H. Adeli, “A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures,” Smart Materials and Structures, vol. 24, no. 12, Article ID 125040, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Zhou, Y. C. Soh, and X. Wu, “Integrated analysis of CFD data with K-means clustering algorithm and extreme learning machine for localized HVAC control,” Applied Thermal Engineering, vol. 76, pp. 98–104, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. View at Publisher · View at Google Scholar
  28. Y. Wu, Y. Zhou, G. Saveriades, S. Agaian, J. P. Noonan, and P. Natarajan, “Local Shannon entropy measure with statistical tests for image randomness,” Information Sciences, vol. 222, pp. 323–342, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. H. H. Bafroui and A. Ohadi, “Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions,” Neurocomputing, vol. 133, pp. 437–445, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. T.-K. Lin and J.-C. Liang, “Application of multi-scale (cross-) sample entropy for structural health monitoring,” Smart Materials and Structures, vol. 24, no. 8, Article ID 085003, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Lay-Ekuakille, P. Vergallo, G. Griffo et al., “Entropy index in quantitative EEG measurement for diagnosis accuracy,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 6, pp. 1440–1450, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. E. Cabal-Yepez, R. J. Romero-Troncoso, A. Garcia-Perez, R. A. Osornio-Rios, and R. Alvarez-Salas, “Multiple fault detection through information entropy analysis in ASD-fed induction motors,” in Proceedings of the 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives (SDEMPED '11), pp. 391–396, Bologna, Italy, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. E. Cabal-Yepez, R. D. J. Romero-Troncoso, A. Garcia-Perez, and C. Rodriguez-Donate, “Novel hardware processing unit for dynamic on-line entropy estimation of discrete time information,” Digital Signal Processing, vol. 20, no. 2, pp. 337–346, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. T. Velmurugan, “Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data,” Applied Soft Computing Journal, vol. 19, pp. 134–146, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. C. T. Yiakopoulos, K. C. Gryllias, and I. A. Antoniadis, “Rolling element bearing fault detection in industrial environments based on a K-means clustering approach,” Expert Systems with Applications, vol. 38, no. 3, pp. 2888–2911, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Andraka, “A survey of CORDIC algorithms for FPGA based computers,” in Proceedings of the ACMSIGDA 6th International Symposium on Field Programmable Gate Arrays (FPGA '98), pp. 191–200, Monterey, Calif, USA, February 1998. View at Publisher · View at Google Scholar
  37. D. Matić, F. Kulić, M. Pineda-Sánchez, and I. Kamenko, “Support vector machine classifier for diagnosis in electrical machines: application to broken bar,” Expert Systems with Applications, vol. 39, no. 10, pp. 8681–8689, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. A. M. Da Silva, R. J. Povinelli, and N. A. O. Demerdash, “Rotor bar fault monitoring method based on analysis of air-gap torques of induction motors,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 2274–2283, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. G. Georgoulas, I. P. Tsoumas, J. A. Antonino-Daviu et al., “Automatic pattern identification based on the complex empirical mode decomposition of the startup current for the diagnosis of rotor asymmetries in asynchronous machines,” IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 4937–4946, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. H. Keskes, A. Braham, and Z. Lachiri, “Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM,” Electric Power Systems Research, vol. 97, pp. 151–157, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. J. J. Rangel-Magdaleno, H. Peregrina-Barreto, J. M. Ramirez-Cortes, P. Gomez-Gil, and R. Morales-Caporal, “FPGA-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphology,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 5, pp. 1032–1040, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. A. Menacer, M. Boumehraz, and H. Cherif, “DWT and Hilbert transform for broken rotor bar fault diagnosis in induction machine at low load,” Energy Procedia, vol. 74, pp. 1248–1257, 2015. View at Google Scholar