[1] |
G. C. Runger, F. B. Alt, and D. C. Montgomery, “Contributors to a multivariate statistical process control chart signal,” Communications in Statistics. Theory and Methods, vol. 25, no. 10, pp. 2203-2213, 1996. · Zbl 0887.62103
· doi:10.1080/03610929608831832 |

[2] |
Y. E. Shao and B. S. Hsu, “Determining the contributors for a multivariate SPC chart signal using artificial neural networks and support vector machine,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 12, pp. 4899-4906, 2009. |

[3] |
C. S. Cheng and H. P. Cheng, “Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 198-206, 2008.
· doi:10.1016/j.eswa.2007.06.002 |

[4] |
H. Y. Huang, Y. E. Shao, C. D. Hou, and M. D. Hsieh, “Identifying the contributors of the multivariate variability control chart using hierarchical support vector machines,” ICIC Express Letters, vol. 5, pp. 3543-3547, 2011. |

[5] |
Y. E. Shao, C. D. Hou, C. H. Chao, and Y. J. Chen, “A decomposition approach for identifying the sources of variance shifts in a multivariate process,” ICIC Express Letters, vol. 5, no. 4 A, pp. 971-975, 2011. |

[6] |
C. C. Chiu, Y. E. Shao, T. S. Lee, and K. M. Lee, “Identification of process disturbance using SPC/EPC and neural networks,” Journal of Intelligent Manufacturing, vol. 14, no. 3-4, pp. 379-388, 2003.
· doi:10.1023/A:1024657911399 |

[7] |
Y. E. Shao and H. D. Hou, “Change point determination for a multivariate process using a two-stage hybrid scheme,” Applied Soft Computing. In press.
· doi:10.1016/j.asoc.2012.02.008 |

[8] |
C. D. Hou, Y. E. Shao, and S. Huang, “A combined MLE and generalized P chart approach to estimate the change point of a multinomial process,” Applied Mathematics & Information Sciences. In press. |

[9] |
R. L. Mason, N. D. Tracy, and J. C. Young, “Decomposition of T2 for multivariate control chart interpretation,” Journal of Quality Technology, vol. 27, no. 2, pp. 99-108, 1995. |

[10] |
R. L. Mason and J. C. Young, “Improving the sensitivity of the T2 statistic in multivariate process control,” Journal of Quality Technology, vol. 31, no. 2, pp. 155-165, 1999. |

[11] |
C. J. Lu, C. M. Wu, C. J. Keng, and C. C. Chiu, “Integrated Application of SPC/EPC/ICA and neural networks,” International Journal of Production Research, vol. 46, no. 4, pp. 873-893, 2008. · Zbl 1160.90420
· doi:10.1080/00207540600943969 |

[12] |
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.
· doi:10.1002/aic.690490414 |

[13] |
J. M. Lee, C. Yoo, and I. B. Lee, “Statistical process monitoring with independent component analysis,” Journal of Process Control, vol. 14, no. 5, pp. 467-485, 2004.
· doi:10.1016/j.jprocont.2003.09.004 |

[14] |
J. M. Lee, C. Yoo, and I. B. Lee, “On-line batch process monitoring using different unfolding method and independent component analysis,” Journal of Chemical Engineering of Japan, vol. 36, no. 11, pp. 1384-1396, 2003.
· doi:10.1252/jcej.36.1384 |

[15] |
C. Xia and J. Howell, “Isolating multiple sources of plant-wide oscillations via independent component analysis,” Control Engineering Practice, vol. 13, no. 8, pp. 1027-1035, 2005. · Zbl 1100.93518
· doi:10.1016/j.conengprac.2004.12.003 |

[16] |
L. Wang and H. B. Shi, “Application of kernel independent component analysis for multivariate statistical process monitoring,” Journal of Donghua University, vol. 26, no. 5, pp. 461-466, 2009. |

[17] |
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.
· doi:10.1016/j.neucom.2010.06.036 |

[18] |
C. H. Wang, T. P. Dong, and W. Kuo, “A hybrid approach for identification of concurrent control chart patterns,” Journal of Intelligent Manufacturing, vol. 20, no. 4, pp. 409-419, 2009.
· doi:10.1007/s10845-008-0115-3 |

[19] |
C. C. Hsu, M. C. Chen, and L. S. Chen, “Integrating independent component analysis and support vector machine for multivariate process monitoring,” Computers and Industrial Engineering, vol. 59, no. 1, pp. 145-156, 2010.
· doi:10.1016/j.cie.2010.03.011 |

[20] |
Y. E. Shao, C. J. Lu, and C. C. Chiu, “A fault detection system for an autocorrelated process using SPC/EPC/ANN and SPC/EPC/SVM schemes,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 9, pp. 5417-5428, 2011. |

[21] |
K. I. Kim, K. Jung, S. H. Park, and H. J. Kim, “Support vector machines for texture classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550, 2002.
· doi:10.1109/TPAMI.2002.1046177 |

[22] |
K. S. Shin, T. S. Lee, and H. J. Kim, “An application of support vector machines in bankruptcy prediction model,” Expert Systems with Applications, vol. 28, no. 1, pp. 127-135, 2005.
· doi:10.1016/j.eswa.2004.08.009 |

[23] |
X. Wang, “Hybrid abnormal patterns recognition of control chart using support vector machining,” in Proceedings of the International Conference on Computational Intelligence and Security (CIS’08), pp. 238-241, December 2008. |

[24] |
S. Y. Lin, R. S. Guh, and Y. R. Shiue, “Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach,” Computers and Industrial Engineering, vol. 61, no. 4, pp. 1123-1134, 2011.
· doi:10.1016/j.cie.2011.06.025 |

[25] |
P. Chongfuangprinya, S. B. Kim, S.-K. Park, and T. Sukchotrat, “Integration of support vector machines and control charts for multivariate process monitoring,” Journal of Statistical Computation and Simulation, vol. 81, no. 9, pp. 1157-1173, 2011. · Zbl 06154460
· doi:10.1080/00949651003789074 |

[26] |
W. Gani, H. Taleb, and M. Limam, “An assessment of the kernel-distance-based multivariate control chart through an industrial application,” Quality and Reliability Engineering International, vol. 27, no. 4, pp. 391-401, 2011.
· doi:10.1002/qre.1117 |

[27] |
J. Park, I. H. Kwon, S. S. Kim, and J. G. Baek, “Spline regression based feature extraction for semiconductor process fault detection using support vector machine,” Expert Systems with Applications, vol. 38, no. 5, pp. 5711-5718, 2011.
· doi:10.1016/j.eswa.2010.10.062 |

[28] |
A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001. |

[29] |
V. D. A. Sánchez, “Frontiers of research in BSS/ICA,” Neurocomputing, vol. 49, pp. 7-23, 2002. · Zbl 1047.68134
· doi:10.1016/S0925-2312(02)00533-7 |

[30] |
V. N. Vapnik, The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, Springer, New York, NY, USA, 2nd edition, 2000. · Zbl 0934.62009 |

[31] |
C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
· doi:10.1109/72.991427 |

[32] |
C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Tech. Rep., Department of Computer Science and Information Engineering, National Taiwan University, 2003. |