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Shock and Vibration
Volume 2016, Article ID 2683269, 13 pages
http://dx.doi.org/10.1155/2016/2683269
Research Article

Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling

1MCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), Rambla San Nebridi No. 22, Gaia Research Building, 08222 Terrassa, Spain
2CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Colonia San Cayetano, 76807 San Juan del Rio, QRO, Mexico
3CA Telematica, DICIS, Universidad de Guanajuato, Carretera Salamanca-Valle km 3.5+1.8, Palo Blanco, 36885 Salamanca, GTO, Mexico

Received 6 May 2016; Revised 18 July 2016; Accepted 18 July 2016

Academic Editor: Mario Terzo

Copyright © 2016 Daniel Zurita-Millán 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. A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Medina-Oliva, P. Weber, and B. Iung, “Industrial system knowledge formalization to aid decision making in maintenance strategies assessment,” Engineering Applications of Artificial Intelligence, vol. 37, pp. 343–360, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Liu, S. F. Ling, and R. Gribonval, “Bearing failure detection using matching pursuit,” NDT & E International, vol. 35, no. 4, pp. 255–262, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. F. Ninoslav, B. Rusmir, and D. Cvetkovic, “Vibration feature extraction methods for gear faults diagnosis—a review,” Facta Universitatis, Series: Working and Living Environmental Protection, vol. 12, no. 1, pp. 63–72, 2015. View at Google Scholar
  5. 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
  6. H. Ozturk, I. Yesilyurt, and M. Sabuncu, “Investigation of effectiveness of some vibration-based techniques in early detection of real-time fatigue failure in gears,” Shock and Vibration, vol. 17, no. 6, pp. 741–757, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. R. B. Randall and J. Antoni, “Rolling element bearing diagnostics—a tutorial,” Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 485–520, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Taplak, S. Erkaya, and I. Uzmay, “Experimental analysis on fault detection for a direct coupled rotor-bearing system,” Measurement, vol. 46, no. 1, pp. 336–344, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Jiang, J. Chen, G. Dong, T. Liu, and W. Xiao, “The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 227, no. 5, pp. 1116–1129, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. M. E. H. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984–993, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. B. T. Holm-Hansen and R. X. Gao, “Vibration analysis of a sensor-integrated ball bearing,” Journal of Vibration and Acoustics, vol. 122, no. 4, pp. 384–392, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, “Rotating machinery prognostics: state of the art, challenges and opportunities,” Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 724–739, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Soualhi, H. Razik, G. Clerc, and D. D. Doan, “Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system,” IEEE Transactions on Industrial Electronics, vol. 61, no. 6, pp. 2864–2874, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. 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
  15. J. Ben Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, and F. Fnaiech, “Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals,” Applied Acoustics, vol. 89, pp. 16–27, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, “Prognostics and health management design for rotary machinery systems—reviews, methodology and applications,” Mechanical Systems and Signal Processing, vol. 42, no. 1-2, pp. 314–334, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Khan, L. Udpa, and S. Udpa, “Particle filter based prognosis study for predicting remaining useful life of steam generator tubing,” in Proceedings of the 2011 IEEE International Conference on Prognostics and Health Management (PHM '11), pp. 1–6, IEEE, Montreal, Canada, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Fan, G. Liu, X. Si, Q. Zhang, and Q. Zhang, “Degradation data-driven approach for remaining useful life estimation,” Journal of Systems Engineering and Electronics, vol. 24, no. 1, pp. 173–182, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Chen, B. Zhang, G. Vachtsevanos, and M. Orchard, “Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering,” IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353–4364, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Wang, C. Li, A. Widodo, P. K. Kankar, and W. Caesarendra, “Fault diagnosis and prognosis of critical components,” Shock and Vibration, vol. 2016, Article ID 9597656, 3 pages, 2016. View at Publisher · View at Google Scholar
  21. Q. Zhang, F. Liu, X. Wan, and G. Xu, “An adaptive support vector regression machine for the state prognosis of mechanical systems,” Shock and Vibration, vol. 2015, Article ID 469165, 8 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. A. C. Chasalevris, P. G. Nikolakopoulos, and C. A. Papadopoulos, “Dynamic effect of bearing wear on rotor-bearing system response,” Journal of Vibration and Acoustics, vol. 135, no. 1, Article ID 011008, 12 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Bianchini, F. Immovilli, M. Cocconcelli, R. Rubini, and A. Bellini, “Fault detection of linear bearings in brushless AC linear motors by vibration analysis,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1684–1694, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. J. A. Carino, D. Zurita, M. Delgado, J. A. Ortega, and R. J. Romero-Troncoso, “Hierarchical classification scheme based on identification, isolation and analysis of conflictive regions,” in Proceedings of the 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA '14), pp. 1–8, IEEE, Barcelona, Spain, September 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Girdhar and C. Scheffer, Practical Machinery Vibration Analysis and Predictive Maintenance, Elsevier, 2004.
  26. J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Wijayasekara, O. Linda, M. Manic, and C. Rieger, “FN-DFE: fuzzy-neural data fusion engine for enhanced resilient state-awareness of hybrid energy systems,” IEEE Transactions on Cybernetics, vol. 44, no. 11, pp. 2065–2075, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Davydenko and R. Fildes, “Measuring forecasting accuracy: the case of judgmental adjustments to SKU-level demand forecasts,” International Journal of Forecasting, vol. 29, no. 3, pp. 510–522, 2013. View at Publisher · View at Google Scholar · View at Scopus