Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 382395, 10 pages
http://dx.doi.org/10.1155/2015/382395
Research Article

Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received 4 July 2014; Revised 31 October 2014; Accepted 5 November 2014

Academic Editor: Yudong Zhang

Copyright © 2015 Min Zhang and Wenming Cheng. 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. D. C. Montgomery, Introduction to Statistical Quality Control, John Wiley & Sons, New York, NY, USA, 2001.
  2. V. Ranaee, A. Ebrahimzadeh, and R. Ghaderi, “Application of the PSOSVM model for recognition of control chart patterns,” ISA Transactions, vol. 49, no. 4, pp. 577–586, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. R.-S. Guh, “A hybrid learning-based model for on-line detection and analysis of control chart patterns,” Computers and Industrial Engineering, vol. 49, no. 1, pp. 35–62, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. S. K. Gauri and S. Chakraborty, “Feature-based recognition of control chart patterns,” Computers & Industrial Engineering, vol. 51, no. 4, pp. 726–742, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. R.-S. Guh and J. D. T. Tannock, “Recognition of control chart concurrent patterns using a neural network approach,” International Journal of Production Research, vol. 37, no. 8, pp. 1743–1765, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. J. H. Yang and M. S. Yang, “A control chart pattern recognition system using a statistical correlation coefficient method,” Computers and Industrial Engineering, vol. 48, no. 2, pp. 205–221, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Chen, S. Lu, and S. Lam, “A hybrid system for SPC concurrent pattern recognition,” Advanced Engineering Informatics, vol. 21, no. 3, pp. 303–310, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-J. Lu, Y. E. Shao, and P.-H. Li, “Mixture control chart patterns recognition using independent component analysis and support vector machine,” Neurocomputing, vol. 74, no. 11, pp. 1908–1914, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Hassan, M. S. Nabi Baksh, A. M. Shaharoun, and H. Jamaluddin, “Improved SPC chart pattern recognition using statistical features,” International Journal of Production Research, vol. 41, no. 7, pp. 1587–1603, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. S. K. Gauri and S. Chakraborty, “Recognition of control chart patterns using improved selection of features,” Computers and Industrial Engineering, vol. 56, no. 4, pp. 1577–1588, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Kano, S. Tanaka, S. Hasebe, I. Hashimoto, and H. Ohno, “Monitoring independent components for fault detection,” AIChE Journal, vol. 49, no. 4, pp. 969–976, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. Q. P. He, S. J. Qin, and J. Wang, “A new fault diagnosis method using fault directions in Fisher discriminant analysis,” AIChE Journal, vol. 51, no. 2, pp. 555–571, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. G. N. Costache, P. Corcoran, and P. Puslecki, “Combining PCA-based datasets without retraining of the basis vector set,” Pattern Recognition Letters, vol. 30, no. 16, pp. 1441–1447, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Wang, Z. Song, and P. Li, “Fault detection behavior and performance analysis of principal component analysis based process monitoring methods,” Industrial & Engineering Chemistry Research, vol. 41, no. 10, pp. 2455–2464, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. J. A. Swift and J. H. Mize, “Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems,” Computers & Industrial Engineering, vol. 28, no. 1, pp. 81–91, 1995. View at Publisher · View at Google Scholar · View at Scopus
  16. C. S. Cheng and N. F. Hubele, “Design of a knowledge-based expert system for statistical process control,” Computers and Industrial Engineering, vol. 22, no. 4, pp. 501–517, 1992. View at Publisher · View at Google Scholar · View at Scopus
  17. H. B. Hwarng and N. F. Hubele, “Back-propagation pattern recognizers for X-bar control charts: methodology and performance,” Computers and Industrial Engineering, vol. 24, no. 2, pp. 219–235, 1993. View at Publisher · View at Google Scholar · View at Scopus
  18. R.-S. Guh and Y.-R. Shiue, “On-line identification of control chart patterns using self-organizing approaches,” International Journal of Production Research, vol. 43, no. 6, pp. 1225–1254, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  19. C. H. Wang, R. S. Guo, M. H. Chiang, and J. Y. Wong, “Decision tree based control chart pattern recognition,” International Journal of Production Research, vol. 46, no. 17, pp. 124–134, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. C.-H. Wang and W. Kuo, “Identification of control chart patterns using wavelet filtering and robust fuzzy clustering,” Journal of Intelligent Manufacturing, vol. 18, no. 3, pp. 343–350, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. S.-Y. Yang, D.-H. Wu, and H.-T. Su, “Abnormal pattern recognition method for control chart based on principal component analysis and support vector machine,” Journal of System Simulation, vol. 18, no. 5, pp. 1314–1318, 2006 (Chinese). View at Google Scholar · View at Scopus
  22. C. Wu and L. Zhao, “Control chart pattern recognition based on wavelet analysis and SVM,” China Mechanical Engineering, vol. 21, no. 13, pp. 1572–1576, 2010 (Chinese). View at Google Scholar · View at Scopus
  23. V. Ranaee and A. Ebrahimzadeh, “Control chart pattern recognition using a novel hybrid intelligent method,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2676–2686, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Zhang and L. Wu, “Classification of fruits using computer vision and a multiclass support vector machine,” Sensors, vol. 12, no. 9, pp. 12489–12505, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Zhang, S. Wang, and G. Ji, “A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 753251, 10 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet