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Mathematical Problems in Engineering
Volume 2016, Article ID 7263285, 8 pages
Research Article

Fault Diagnosis Method on Polyvinyl Chloride Polymerization Process Based on Dynamic Kernel Principal Component and Fisher Discriminant Analysis Method

1College of Information and Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
2National Financial Security and System Equipment Engineering Research Center, University of Science & Technology Liaoning, Anshan 114044, China
3Beijing Institute of Technology, School of Software, Beijing 100081, China

Received 8 July 2016; Accepted 29 September 2016

Academic Editor: Yaguo Lei

Copyright © 2016 Shu-zhi Gao 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. S. Z. Gao, X. W. Gao, J. S. Wang, and P. C. Fei, “Rough set-neural network fault diagnosis of polymerization based on improved attribute reduction algorithm of discernibility matrix,” Journal of Chemical Industry and Engineering, vol. 62, no. 3, pp. 759–765, 2011. View at Google Scholar
  2. Y. H. Gao and Z. Yang, “An application of PCA for monitoring and diagnosing fault in a chemical polymeric process,” Journal of Southern Yangtze University, vol. 4, no. 4, pp. 352–356, 2005. View at Google Scholar
  3. G. W. Dou and A. L. Liu, “Fault detection based on kernel principal component analysis,” Chinese Journal of Scientific Instrument, vol. 30, no. 6, pp. 443–447, 2009. View at Google Scholar
  4. H. Song, H. Zhang, and X. Wang, “Multiple faults diagnosis approach for nonlinear system,” Journal of Beijing University of Aeronautics and Astronautics, vol. 31, no. 11, pp. 1198–1203, 2005. View at Google Scholar · View at Scopus
  5. S.-H. Jiang, W.-H. Gui, C.-H. Yang, and Z.-H. Tang, “Method based on kernel principal component analysis and support vector machine and its application,” Journal of Central South University (Science and Technology), vol. 40, no. 5, pp. 1323–1328, 2009. View at Google Scholar · View at Scopus
  6. L. Li, J. N. Zhu, and H. B. Shi, “Fault detection of chemical process based on multiscale dynamic kernel principal component analysis, control and instruments in chemical industry,” Control and Instruments in Chemical Industry, vol. 35, no. 4, pp. 23–26, 2008. View at Google Scholar
  7. W. Ku, R. H. Storer, and C. Georgakis, “Disturbance detection and isolation by dynamic principal component analysis,” Chemometrics and Intelligent Laboratory Systems, vol. 30, no. 1, pp. 179–196, 1995. View at Publisher · View at Google Scholar · View at Scopus
  8. V. Makis, J. Wu, and Y. Gao, “An application of DPCA to oil data for CBM modeling,” European Journal of Operational Research, vol. 174, no. 1, pp. 112–123, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. M. A. Kramer, “Nonlinear principal component analysis using autoassociative neural networks,” AIChE Journal, vol. 37, no. 2, pp. 233–243, 1991. View at Publisher · View at Google Scholar
  10. D. Dong and T. J. McAvoy, “Nonlinear principal component analysis—based on principal curves and neural networks,” Computers & Chemical Engineering, vol. 20, no. 1, pp. 65–78, 1996. View at Publisher · View at Google Scholar
  11. L. Jong-Min, C. K. Yoo, S. W. Choi et al., “Nonlinear Proeess monitoring using kemel Prineipal component analysis,” Chemieal Engineering Seienee, vol. 59, 2004. View at Google Scholar
  12. H. T. Shi, J. C. Liu, X. D. Ding, and S. Tan, “Fault detection based on hybrid dynamic principal component analysis,” Control Engineering of China, vol. 19, no. 1, pp. 148–150, 2012. View at Google Scholar
  13. Z. Q. Bian and X. G. Zhang, Pattern Recognition, Tsinghua University Press, Beijing, China, 1999.
  14. H. H. Xin, Process Monitoring based on Fisher Disciminant Analysis, China University of Petroleum (East China), 2011.
  15. N. Lv, Process Monitoring based on Fisher Disciminant Analysis, Harbin University of Science and Technology, 2009.
  16. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Advanced Textbooks in Control and Signal Processing, Springer, London, UK, 2001. View at Publisher · View at Google Scholar