Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2018, Article ID 5382398, 14 pages
https://doi.org/10.1155/2018/5382398
Research Article

A SVDD and -Means Based Early Warning Method for Dual-Rotor Equipment under Time-Varying Operating Conditions

College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

Correspondence should be addressed to Kun Feng; moc.361@dhpgnefnuk

Received 15 August 2017; Revised 4 December 2017; Accepted 12 December 2017; Published 4 January 2018

Academic Editor: Sandris Ručevskis

Copyright © 2018 Zhinong Jiang 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. R. Depold and J. Siegel, “Using diagnostics and prognostics to minimize the cost of ownership of gas turbines,” in Proceedings of the 2006 ASME 51st Turbo Expo, pp. 845–851, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Hindle, R. Van Stone, C. Brogan, J. Vandike, K. Dale, and N. Gibson, “A prognostic and diagnostic approach to engine health management,” in Proceedings of the 2006 ASME 51st Turbo Expo, pp. 673–680, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Balevic, “Heavy-duty gas turbine operating and maintenance considerations,” GER, 2003. View at Google Scholar
  4. J. B. 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
  5. A. Widodo and B. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  7. B.-S. Yang, T. Han, and W.-W. Hwang, “Fault diagnosis of rotating machinery based on multi-class support vector machines,” Journal of Mechanical Science and Technology, vol. 19, no. 3, pp. 846–859, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Aydin, M. Karakose, and E. Akin, “A multi-objective artificial immune algorithm for parameter optimization in support vector machine,” Applied Soft Computing, vol. 11, no. 1, pp. 120–129, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Lee and K.-K. Seo, “Intelligent fault diagnosis based on a hybrid multi-class support vector machines and case-based reasoning approach,” Journal of Computational and Theoretical Nanoscience, vol. 10, no. 8, pp. 1727–1734, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. Yin and J. Hou, “Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes,” Neurocomputing, vol. 174, pp. 643–650, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. K. R. Fyfe and E. D. S. Munck, “Analysis of computed order tracking,” Mechanical Systems and Signal Processing, vol. 11, no. 2, pp. 187–202, 1997. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Borghesani, R. Ricci, S. Chatterton, and P. Pennacchi, “A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions,” Mechanical Systems and Signal Processing, vol. 38, no. 1, pp. 23–35, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Jiang, M. Hu, K. Feng, and Y. He, “Weak fault feature extraction scheme for intershaft bearings based on linear prediction and order tracking in the rotation speed difference domain,” Applied Sciences-Basel, vol. 7, no. 937, 2017. View at Google Scholar
  14. G. Q. Ren, W.-C. Zhang, and B. Li, “Research on the gearbox fault signal relation to the sensitivity of variable working conditions,” Applied Mechanics and Materials, vol. 494-495, pp. 921–924, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Gu, L. Song, T. Xu, L. Su, and G. Wu, “Research on Wind Turbine Gearbox Fault Warning Method under Variable Operational Condition,” Acta Press, 2014. View at Google Scholar
  16. C. Lin and V. Makis, “Optimal Bayesian maintenance policy and early fault detection for a gearbox operating under varying load,” Journal of Vibration and Control, vol. 22, no. 15, pp. 3312–3325, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. Y. Shao, K. M. Chris, J. Ou, and Y. Hu, “Gearbox deterioration detection under steady state, variable load, and variable speed conditions,” Chinese Journal of Mechanical Engineering, vol. 22, no. 2, pp. 256–264, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Kouadri, G. R. Ibrahim, and A. Albarbar, “Varying load detection in a gearbox system based on adaptive threshold estimation,” in Proceedings of the International Conference on Mechanical, Manufacturing, Modeling and Mechatronics, IC4M 2016, Kuala Lumpur, Malaysia, February 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Fu and A. Robles-Kelly, “On mixtures of linear svms for nonlinear classification,” Joint Iapr International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, vol. 5342, pp. 489–499, 2008. View at Publisher · View at Google Scholar
  20. J. A. K. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle, “Weighted least squares support vector machines: robustness and sparce approximation,” Neurocomputing, vol. 48, pp. 85–105, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. Q.-A. Tran, X. Li, and H. Duan, “Efficient performance estimate for one-class support vector machine,” Pattern Recognition Letters, vol. 26, no. 8, pp. 1174–1182, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribution,” Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. D. M. J. Tax and R. P. W. Duin, “Support vector domain description,” Pattern Recognition Letters, vol. 20, no. 11–13, pp. 1191–1199, 1999. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. N. Can, “Modeling and fault prediction of complex system support vector machine,” National Defense industry Press, 2015. View at Google Scholar
  25. O. L. Mangasarian and M. E. Thompson, “Chunking for massive nonlinear kernel classification,” Optimization Methods & Software, vol. 23, no. 3, pp. 365–374, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  26. K. R. Žalik, “An efficient k-means clustering algorithm,” Pattern Recognition Letters, vol. 29, no. 9, pp. 1385–1391, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognition Letters, vol. 31, no. 8, pp. 651–666, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proc. of Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967. View at Google Scholar · View at MathSciNet
  29. V. S. Ananthanarayana, M. N. Murty, and D. K. Subramanian, “Efficient clustering of large data sets,” Pattern Recognition, vol. 34, no. 12, pp. 2561–2563, 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognition, vol. 36, no. 2, pp. 451–461, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. X. Xu, SVM Parameter Optimization and Its Application in the Classification [Ph.D. thesis], Dalian Maritime University, Dalian City, China, December 2014.
  32. X. Liu, D. Jia, and H. Li, “Research on Kernel Parameter Optimization of Support Vector Machine in Speaker Recognition,” Science Technology and Engineering, vol. 10, no. 7, pp. 1669–1673, 2010. View at Google Scholar
  33. P. Chen, J. Wang, and H. Lee, “Model selection Of SVMs using GA approach,” in Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Piscataway, NJ, USA, 2004.
  34. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science (MHS '95), Piscataway, NJ, USA, October 1995. View at Scopus
  35. T. Xiao, D. Ren, S. Lei et al., “Based on grid-search and PSO parameter optimization for Support Vector Machine,” Intelligent Control and Automation, IEEE, 2015. View at Google Scholar
  36. D. W. Coit, “Genetic algorithms and engineering design,” The Engineering Economist, vol. 43, no. 4, pp. 379–381, 1998. View at Publisher · View at Google Scholar
  37. D. E. Goldberg and J. H. Holland, “Genetic algorithms and machine learning,” Machine Learning, vol. 3, no. 2-3, pp. 95–99, 1998. View at Publisher · View at Google Scholar
  38. K. Muthulakshmi, R. M. Sasiraja, and V. Suresh Kumar, “The proper location and sizing of multiple distributed generators for maximizing voltage stability using PSO,” Journal of Circuits, Systems and Computers, vol. 26, no. 4, Article ID 1750057, 2017. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995. View at Scopus
  40. P. C. Bhat, H. B. Prosper, S. Sekmen, and C. Stewart, “Optimizing event selection with the random grid search,” High Energy Physics-Phenomenology, 2017, High Energy Physics-Phenomenology. View at Google Scholar
  41. “Siemens, LMS SCADAS [EB/OL],” 2017, https://www.plm.automation.siemens.com/zh/products/lms/testing//scadas/lab.shtml.
  42. ISO 10816-4:2009, Mechanical vibration, Evaluation of machine vibration by measurements on non-rotating.