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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 939248, 9 pages
http://dx.doi.org/10.1155/2015/939248
Research Article

An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

IE Department, The University of Jordan, Amman 11942, Jordan

Received 7 May 2015; Accepted 6 July 2015

Academic Editor: José David Martín-Guerrero

Copyright © 2015 Mahmoud Barghash. 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. H. Bonnett and G. C. Soukup, “Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors,” IEEE Transactions on Industry Applications, vol. 28, no. 4, pp. 921–937, 1992. View at Publisher · View at Google Scholar · View at Scopus
  2. H. P. Rodríguez, J. B. Alonso, M. A. Ferrer, and C. M. Travieso, “Application of the Teager-Kaiser energy operator in bearing fault diagnosis,” ISA Transactions, vol. 52, no. 2, pp. 278–284, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Theilliol, H. Noura, D. Sauter, and F. Hamelin, “Sensor fault diagnosis based on energy balance evaluation: application to a metal processing,” ISA Transactions, vol. 45, no. 4, pp. 603–610, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. L. S. Nelson, “The Shewart control chart-tests for special causes,” Journal of Quality Technology, vol. 16, pp. 237–239, 1984. View at Google Scholar · View at Scopus
  5. M. A. Barghash, “A diverse neural network ensemble team for mean shift detection in X-Bar and CUSUM control charts,” Jordan Journal of Mechanical and Industrial Engineering, vol. 5, no. 4, pp. 291–300, 2011. View at Google Scholar · View at Scopus
  6. M. A. Barghash and N. S. Santarisi, “Pattern recognition of control charts using artificial neural networks—analyzing the effect of the training parameters,” Journal of Intelligent Manufacturing, vol. 15, no. 5, pp. 635–644, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-S. Cheng, “A multi-layer neural network model for detecting changes in the process mean,” Computers & Industrial Engineering, vol. 28, no. 1, pp. 51–61, 1995. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-S. Cheng, “A neural network approach for the analysis of control chart patterns,” International Journal of Production Research, vol. 35, no. 3, pp. 667–697, 1997. View at Publisher · View at Google Scholar · View at Scopus
  9. 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
  10. D. T. Pham and E. Oztemel, “Control chart pattern recognition using neural networks,” Journal of Systems Engineering, vol. 2, no. 4, pp. 256–262, 1992. View at Google Scholar
  11. G. A. Pugh, “A comparison of neural networks to SPC charts,” Computers & Industrial Engineering, vol. 21, no. 1–4, pp. 253–255, 1991. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Santarisi and M. A. Barghash, “Neuro-fuzzy based model for pattern recognition in control charts,” in Proceedings of the 37th International Conference on Computers and Industrial Engineering, pp. 1881–1897, October 2007. View at Scopus
  13. X. Shao, “Recognition of control chart patterns using decision tree of multi-class SVM,” in Advances in Intelligent Systems, vol. 138 of Advances in Intelligent and Soft Computing, pp. 33–41, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  14. D. T. Pham and M. A. Wani, “Feature-based control chart pattern recognition,” International Journal of Production Research, vol. 35, no. 7, pp. 1875–1890, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. D. T. Pham and E. Oztemel, “XPC: an on-line expert system for statistical process control,” International Journal of Production Research, vol. 30, no. 12, pp. 2857–2872, 1992. View at Google Scholar
  16. M. Bag, S. K. Gauri, and S. Chakraborty, “An expert system for control chart pattern recognition,” The International Journal of Advanced Manufacturing Technology, vol. 62, no. 1–4, pp. 291–301, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. S. I. Chang and C. A. Aw, “A neural fuzzy control chart for detecting and classifying process mean shifts,” International Journal of Production Research, vol. 34, no. 8, pp. 2265–2278, 1996. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  18. S. I. Chang and E. S. Ho, “Two-stage neural network approach for process variance change detection and classification,” International Journal of Production Research, vol. 37, no. 7, pp. 1581–1599, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. S. K. Gauri and S. Chakraborty, “Recognition of control chart patterns using improved selection of features,” Computers & Industrial Engineering, vol. 56, no. 4, pp. 1577–1588, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. S. K. Gauri and S. Chakraborty, “Feature-based recognition of control chart patterns,” Computers and Industrial Engineering, vol. 51, no. 4, pp. 726–742, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Gülbay and C. Kahraman, “Development of fuzzy process control charts and fuzzy unnatural pattern analyses,” Computational Statistics & Data Analysis, vol. 51, no. 1, pp. 434–451, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. H. B. Hwarng and C. W. Chong, “Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer,” International Journal of Production Research, vol. 33, no. 7, pp. 1817–1833, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  23. D. T. Pham and E. Oztemel, “Control chart pattern recognition using learning vector quantization networks,” International Journal of Production Research, vol. 32, no. 3, pp. 721–729, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. J. Plummer, “Tighter process control with neural networks,” AI Expert, vol. 8, pp. 49–55, 1993. View at Google Scholar
  25. V. Ranaee and A. Ebrahimzadeh, “Control chart pattern recognition using an optimized neural network and efficient features,” ISA Transactions, vol. 49, no. 3, pp. 387–393, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Wu and J.-B. Yu, “A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes,” Expert Systems with Applications, vol. 37, no. 6, pp. 4058–4065, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. W.-A. Yang and W. Zhou, “Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble,” Journal of Intelligent Manufacturing, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. J. B. Yu, L. F. Xi, and B. Wu, “A neural network ensemble approach for the recognition of SPC chart patterns,” in Proceedings of the 3rd International Conference on Natural Computation, vol. 2, pp. 575–579, 2007.
  29. J. B. Yu, L. F. Xi, and X. Zhou, “Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble,” Engineering Applications of Artificial Intelligence, vol. 22, no. 1, pp. 141–152, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Du, J. Lv, and L. Xi, “On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines,” International Journal of Production Research, vol. 50, no. 22, pp. 6288–6310, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. T.-F. Li, S. Hu, Z.-Y. Wei, and Z.-Q. Liao, “A framework for diagnosing the out-of-control signals in multivariate process using optimized support vector machines,” Mathematical Problems in Engineering, vol. 2013, Article ID 494626, 9 pages, 2013. View at Publisher · View at Google Scholar
  32. W.-A. Yang, “Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks,” Journal of Intelligent Manufacturing, 2013. View at Publisher · View at Google Scholar
  33. J. Yu and L. Xi, “A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes,” International Journal of Production Research, vol. 47, no. 15, pp. 4077–4108, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  34. A. Hassan, “Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns,” in Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM '08), pp. 1996–2000, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Barghash and N. Santarisi, “A study on the response and stability of artificial neural networks for pattern recognition in control charts,” in Proceedings of the 12th International Conference on Machine Design and Production, Kuşadası, Turkey, September 2006.
  36. S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan, Englewood Clifs, NJ, USA, 1994.