Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2016 (2016), Article ID 3530464, 11 pages
http://dx.doi.org/10.1155/2016/3530464
Research Article

Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

1Electronics Department, National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, 72840 Tonantzintla, PUE, Mexico
2Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, 72840 Tonantzintla, PUE, Mexico
3Technological Institute of Apizaco, 90300 Apizaco, TLAX, Mexico
4National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, 72840 Tonantzintla, PUE, Mexico

Received 23 October 2015; Revised 4 January 2016; Accepted 10 January 2016

Academic Editor: Daniel Morinigo-Sotelo

Copyright © 2016 Jose Rangel-Magdaleno 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. R. Yan, R. X. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: a review with applications,” Signal Processing, vol. 96, part A, pp. 1–15, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. J. D. J. Rangel-Magdaleno, R. D. J. Romero-Troncoso, R. A. Osornio-Rios, E. Cabal-Yepez, and L. M. Contreras-Medina, “Novel methodology for online half-broken-bar detection on induction motors,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 5, pp. 1690–1698, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. K. N. Gyftakis, D. V. Spyropoulos, J. C. Kappatou, and E. D. Mitronikas, “A novel approach for broken bar fault diagnosis in induction motors through torque monitoring,” IEEE Transactions on Energy Conversion, vol. 28, no. 2, pp. 267–277, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. 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
  5. H. Razik, M. B. de Rossiter Corrêa, and E. R. C. da Silva, “A novel monitoring of load level and broken bar fault severity applied to squirrel-cage induction motors using a genetic algorithm,” IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4615–4626, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Ordaz-Moreno, R. D. J. Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, and A. Garcia-Perez, “Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation,” IEEE Transactions on Industrial Electronics, vol. 55, no. 5, pp. 2193–2202, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Nemec, K. Drobnič, D. Nedeljković, R. Fišer, and V. Ambrožič, “Detection of broken bars in induction motor through the analysis of supply voltage modulation,” IEEE Transactions on Industrial Electronics, vol. 57, no. 8, pp. 2879–2888, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Z. Li, W. Wang, and F. Ismail, “A spectrum synch technique for induction motor health condition monitoring,” IEEE Transactions on Energy Conversion, vol. 30, no. 4, pp. 1348–1355, 2015. View at Publisher · View at Google Scholar
  9. J. Faiz, V. Ghorbanian, and B. M. Ebrahimi, “EMD-based analysis of industrial induction motors with broken rotor bars for identification of operating point at different supply modes,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 957–966, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Sapena-Baño, M. Pineda-Sanchez, R. Puche-Panadero, J. Martinez-Roman, and D. Matic, “Fault diagnosis of rotating electrical machines in transient regime using a single stator current’s FFT,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 11, pp. 3137–3146, 2015. View at Publisher · View at Google Scholar
  11. A. Sadeghian, Z. Ye, and B. Wu, “Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 7, pp. 2253–2263, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Su, K. T. Chong, and R. Ravi Kumar, “Vibration signal analysis for electrical fault detection of induction machine using neural networks,” Neural Computing and Applications, vol. 20, no. 2, pp. 183–194, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Gritli, A. O. Di Tommaso, R. Miceli, F. Filippetti, and C. Rossi, “Closed-loop bandwidth impact on MVSA for rotor broken bar diagnosis in IRFOC double squirrel cage induction motor drives,” in Proceedings of the International Conference on Clean Electrical Power (ICCEP '13), pp. 529–534, IEEE, Alghero, Italy, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Deng and R. Zhao, “A vibration analysis method based on hybrid techniques and its application to rotating machinery,” Measurement, vol. 46, no. 9, pp. 3671–3682, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. 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
  16. G. Betta, C. Liguori, A. Paolillo, and A. Pietrosanto, “A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 51, no. 6, pp. 1316–1321, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Sadoughi, M. Ebrahimi, M. Moalem, and S. Sadri, “Intelligent diagnosis of broken bars in induction motors based on new features in vibration spectrum,” in Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED '07), pp. 106–111, IEEE, Cracow, Poland, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. L. M. R. Baccarini, V. V. Rocha e Silva, B. R. de Menezes, and W. M. Caminhas, “SVM practical industrial application for mechanical faults diagnostic,” Expert Systems with Applications, vol. 38, no. 6, pp. 6980–6984, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Sadoughi, M. Ebrahimi, and E. Rezaei, “A new approach for induction motor broken bar diagnosis by using vibration spectrum,” in Proceedings of the SICE-ICASE International Joint Conference, pp. 4715–4720, Busan, Republic of Korea, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. J. A. Antonino-Daviu, J. Pons-Llinares, V. Climente-Alarcon, and H. Razik, “Evaluation of startup-based rotor fault severity indicators under different starting methods,” in Proceedings of the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON '14), pp. 3361–3366, Dallas, Tex, USA, October-November 2014. View at Publisher · View at Google Scholar
  21. H. Su and K. T. Chong, “Induction machine condition monitoring using neural network modeling,” IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 241–249, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. P. J. Rodriguez, A. Belahcen, and A. Arkkio, “Signatures of electrical faults in the force distribution and vibration pattern of induction motors,” IEE Proceedings: Electric Power Applications, vol. 153, no. 4, pp. 523–529, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. P. C. M. L. Filho, J. N. Brito, V. A. D. Silva, and R. Pederiva, “Detection of electrical faults in induction motors using vibration analysis,” Journal of Quality in Maintenance Engineering, vol. 19, no. 4, pp. 364–380, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Miceli, Y. Gritli, A. di Tommaso, F. Filippetti, and C. Rossi, “Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis,” COMPEL, vol. 33, no. 5, pp. 1625–1641, 2014. View at Google Scholar
  25. V. Climente-Alarcon, J. A. Antonino-Daviu, F. Vedreño-Santos, and R. Puche-Panadero, “Vibration transient detection of broken rotor bars by PSH sidebands,” IEEE Transactions on Industry Applications, vol. 49, no. 6, pp. 2576–2582, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Yan, R. X. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: a review with applications,” Signal Processing, vol. 96, pp. 1–15, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Garcia-Perez, R. J. Romero-Troncoso, E. Cabal-Yepez, R. A. Osornio-Rios, J. D. J. Rangel-Magdaleno, and H. Miranda, “Startup current analysis of incipient broken rotor bar in induction motors using high-resolution spectral analysis,” in Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (SDEMPED '11), pp. 657–663, IEEE, Bologna, Italy, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Samet, T. Ghanbari, and M. Ahmadi, “An auto-correlation function based technique for discrimination of internal fault and magnetizing inrush current in power transformers,” Electric Power Components and Systems, vol. 43, no. 4, pp. 399–411, 2015. View at Publisher · View at Google Scholar
  30. T. Ghanbari, “Autocorrelation function-based technique for stator turn-fault detection of induction motor,” IET Science, Measurement & Technology, 2015. View at Publisher · View at Google Scholar
  31. T. Hastie, J. Friedman, and R. Tibshirani, The Elements of Statistical Learning, Springer Series in Statistics, Springer, New York, NY, USA, 2001. View at Publisher · View at Google Scholar · View at MathSciNet