zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
A hybrid ICA-SVM approach for determining the quality variables at fault in a multivariate process. (English) Zbl 1264.94066
Summary: The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC) charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA) and support vector machine (SVM) to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling $T^{2}$ MSPC chart to generate independent components (ICs). The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

94A12Signal theory (characterization, reconstruction, filtering, etc.)
62P30Applications of statistics in engineering and industry
62H30Classification and discrimination; cluster analysis (statistics)
Full Text: DOI
[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.