Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2017 (2017), Article ID 6103947, 8 pages
https://doi.org/10.1155/2017/6103947
Research Article

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

1Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
2School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou 450001, China

Correspondence should be addressed to Lei Chen; nc.ude.uzz@ielnehc

Received 6 October 2016; Revised 6 February 2017; Accepted 21 February 2017; Published 12 March 2017

Academic Editor: Hassan Askari

Copyright © 2017 Lei Chen 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. J. Han and L. D. Shi, Full Vector Spectrum Technology and Its Engineering Application, China Machine Press, Beijing, China, 2008.
  2. L. Chen, J. Han, W. Lei, Y. Cui, and Z. Guan, “Full-vector signal acquisition and information fusion for the fault prediction,” International Journal of Rotating Machinery, vol. 2016, Article ID 5980802, 7 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Ma, X. L. Xu, and D. H. Zhou, “Survey of fault prediction methods for rotating machinery,” Process Automation Instrumentation, vol. 32, no. 8, pp. 1–3, 2011. View at Google Scholar
  4. S. Su, W. Zhang, and S. Zhao, “Fault prediction for nonlinear system using sliding ARMA combined with online LS-SVR,” Mathematical Problems in Engineering, vol. 2014, Article ID 692848, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Zhang, Y. Wang, and K. Wang, “Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks,” International Journal of Advanced Manufacturing Technology, vol. 68, no. 1–4, pp. 763–773, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. K. Wang, J. Deng, and Y. Yang, “A fault prediction method of rotating machinery based on an improved empirical mode decomposition,” Advanced Science Letters, vol. 5, no. 2, pp. 874–877, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. A. K. Verma, S. Sarangi, and M. Kolekar, “Misalignment Fault Prediction of Motor-Shaft Using Multiscale Entropy and Support Vector Machine,” Advances in Intelligent Systems and Computing, vol. 320, pp. 359–370, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Kiakojoori and K. Khorasani, “Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis,” Neural Computing and Applications, vol. 27, no. 8, pp. 2157–2192, 2016. View at Publisher · View at Google Scholar
  9. P. Kesaba, B. B. Choudhury, and M. K. Muni, “An Effective prediction of position analysis of industrial robot using fuzzy logic approach,” in Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, vol. 43 of Smart Innovation, Systems and Technologies, pp. 45–54, Springer, New Delhi, India, 2016. View at Publisher · View at Google Scholar
  10. S. K. Yang and T. S. Liu, “State estimation for predictive maintenance using Kalman filter,” Reliability Engineering and System Safety, vol. 66, no. 1, pp. 29–39, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Ray and S. Tangirala, “Stochastic modeling of fatigue crack dynamics for on-line failure prognostics,” IEEE Transactions on Control Systems Technology, vol. 4, no. 4, pp. 443–451, 1996. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Li, S. J. Qin, Y. Ji, and D. Zhou, “Reconstruction based fault prognosis for continuous processes,” Control Engineering Practice, vol. 18, no. 10, pp. 1211–1219, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. C. M. Yam, P. W. Tse, L. Li, and P. Tu, “Intelligent predictive decision support system for condition-based maintenance,” International Journal of Advanced Manufacturing Technology, vol. 17, no. 5, pp. 383–391, 2001. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Samanta and C. Nataraj, “Prognostics of machine condition using energy based monitoring index and computational intelligence,” in Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE '08), vol. 3, pp. 1347–1358, Brooklyn, NY, USA, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. L.-H. Lin, J. Ma, and X.-L. Xu, “The turbine machine fault prediction based on kernel principal component analysis,” Advanced Materials Research, vol. 383-390, pp. 4787–4791, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Verma, S. Sarangi, and M. H. Kolekar, “Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods,” Computers and Electrical Engineering, vol. 40, no. 7, pp. 2246–2258, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. D. E. Bently, C. T. Hatch, and B. Grissom, Fundamentals of Rotating Machinery Diagnostics, Bently Pressurized Bearing Press, Minden Nev, USA, 2002.
  18. L. S. Qu, Holospectrum and Holobalancing Technique in Machinery Diagnosis, Beijing Science Press, Beijing, China, 2007.
  19. A. Muszynska, Rotordynamics, Taylor Francis Group, New York, NY, USA, 2005.
  20. J. P. Nolan and N. Ravishanker, “Simultaneous prediction intervals for ARMA processes with stable innovations,” Journal of Forecasting, vol. 28, no. 3, pp. 235–246, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. D. H. Yi, Time Series Analysis Method and Application, China Renmin University Press, Beijing, China, 2011.
  22. Y. Lu and F. Gao, “A novel time-domain auto-regressive model for structural damage diagnosis,” Journal of Sound and Vibration, vol. 283, no. 3–5, pp. 1031–1049, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Akaike, “A Bayesian analysis of the minimum AIC procedure,” Annals of the Institute of Statistical Mathematics, vol. 30, no. 1, pp. 9–14, 1978. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. H. Akaike, “Maximum likelihood identification of Gaussian autoregressive moving average models,” Biometrika, vol. 60, pp. 255–265, 1973. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  25. A. Spanos, “Akaike-type criteria and the reliability of inference: model selection versus statistical model specification,” Journal of Econometrics, vol. 158, no. 2, pp. 204–220, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  26. M. Piłatowska, “Information and prediction criteria in selecting the forecasting model,” Dynamic Econometric Models, vol. 11, pp. 21–40, 2011. View at Publisher · View at Google Scholar
  27. G. X. Wu, “Comment and improvement on criteria of order determination of sequence model,” Journal of Xian Mining Institute, vol. 15, no. 4, pp. 307–309, 1995. View at Google Scholar
  28. J. M. Vance, Rotordynamics of Turbomachinery, Wiley-Interscience, New York, NY, USA, 1987.