D. C. Montgomery and G. C. Runger, Applied statistics and probability for engineers, 2003.

D. C. Montgomery, Introduction to statistical quality control, 2005.

W. E. Company, Statistical quality control handbook, Western Electric, 1958.

L. S. Nelson, The shewhart control chart -tests for special causes, Journal of Quality Technology, vol.16, issue.4, pp.237-239, 1984.

H. Cheng and C. Cheng, Control chart pattern recognition using wavelet analysis and neural networks, Journal of Quality, vol.16, issue.5, pp.311-321, 2009.

V. Ranaee and A. Ebrahimzadeh, Control chart pattern recognition using a novel hybrid intelligent method, Applied Soft Computing, vol.11, issue.2, pp.2676-2686, 2011.
DOI : 10.1016/j.asoc.2010.10.016

D. T. Pham and M. A. Wani, Feature-based control chart pattern recognition, International Journal of Production Research, vol.35, issue.7, pp.1875-1890, 1997.
DOI : 10.1080/002075497194967

V. Ranaee, A. Ebrahimzadeh, and R. Ghaderi, Application of the PSO???SVM model for recognition of control chart patterns, ISA Transactions, vol.49, issue.4, pp.577-586, 2010.
DOI : 10.1016/j.isatra.2010.06.005

S. K. Gauri, Control chart pattern recognition using feature-based learning vector quantization, The International Journal of Advanced Manufacturing Technology, vol.43, issue.9-12, pp.1061-1073, 2010.
DOI : 10.1007/s00170-009-2354-7

S. K. Gauri and S. Chakraborty, Recognition of control chart patterns using improved selection of features, Computers & Industrial Engineering, vol.56, issue.4, pp.1577-1588, 2009.
DOI : 10.1016/j.cie.2008.10.006

M. Bag, S. K. Gauri, and S. Chakraborty, Recognition of control chart patterns using discriminant analysis of shape features, International Conference on Industrial Engineering and Operations Management, 2010.

M. Zhang and W. Cheng, Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features, Mathematical Problems in Engineering, vol.21, issue.13
DOI : 10.1155/2013/753251

C. Wu, F. Liu, and B. Zhu, Control chart pattern recognition using an integrated model based on binary-tree support vector machine, International Journal of Production Research, vol.18, issue.6, pp.2026-2040, 2015.
DOI : 10.1080/00207543.2011.623724

P. Xanthopoulos and T. Razzaghi, A weighted support vector machine method for control chart pattern recognition, Computers & Industrial Engineering, vol.70, pp.134-149, 2014.
DOI : 10.1016/j.cie.2014.01.014

R. Guh and J. D. Tannock, Recognition of control chart concurrent patterns using a neural network approach, International Journal of Production Research, vol.37, issue.8, pp.1743-1765, 1999.
DOI : 10.1080/002075499190987

W. Yang, W. Zhou, W. Liao, and Y. Guo, Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines, Neurocomputing, vol.147, pp.260-270, 2015.
DOI : 10.1016/j.neucom.2014.06.068

Z. Chen, S. Lu, and S. Lam, A hybrid system for SPC concurrent pattern recognition, Advanced Engineering Informatics, vol.21, issue.3, pp.303-310, 2007.
DOI : 10.1016/j.aei.2007.03.002

J. Yang and M. Yang, A control chart pattern recognition system using a statistical correlation coefficient method, Computers & Industrial Engineering, vol.48, issue.2, 2005.
DOI : 10.1016/j.cie.2005.01.008

S. Du, D. Huang, and J. Lv, Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines, Computers & Industrial Engineering, vol.66, issue.4, pp.683-695, 2013.
DOI : 10.1016/j.cie.2013.09.012

N. Gu, Z. Cao, L. Xie, D. Creighton, M. Tan et al., Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization, Journal of Intelligent Manufacturing, vol.4, issue.5, pp.1241-1252, 2013.
DOI : 10.1007/s10845-012-0659-0

L. Xie, N. Gu, D. Li, Z. Cao, M. Tan et al., Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine, Computers & Industrial Engineering, vol.64, issue.1, pp.280-289, 2013.
DOI : 10.1016/j.cie.2012.10.009

A. Hyvärinen, J. Karhunen, and E. Oja, Independent component analysis, 2001.

P. Comon and C. Jutten, Handbook of blind source separation: independent component analysis and applications, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00460653

C. Wang, T. Dong, and W. Kuo, A hybrid approach for identification of concurrent control chart patterns, Journal of Intelligent Manufacturing, vol.47, issue.11, pp.409-419, 2009.
DOI : 10.1007/s10845-008-0115-3

C. Lu, Y. E. Shao, and P. Li, Mixture control chart patterns recognition using independent component analysis and support vector machine, Neurocomputing, vol.74, issue.11, pp.1908-1914, 2011.
DOI : 10.1016/j.neucom.2010.06.036

C. Jutten and J. Hérault, Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, vol.24, issue.1, pp.1-10, 1991.
DOI : 10.1016/0165-1684(91)90079-X

G. Darmois, Analyse générale des liaisons stochastiques, pp.2-8, 1953.

P. Comon, Independent component analysis, A new concept?, Signal Processing, vol.36, issue.3, pp.287-314, 1994.
DOI : 10.1016/0165-1684(94)90029-9

URL : https://hal.archives-ouvertes.fr/hal-00417283

J. F. Cardoso, Infomax and maximum likelihood for blind source separation, IEEE Signal Processing Letters, vol.4, issue.4, pp.112-114, 1997.
DOI : 10.1109/97.566704

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. F. Cardoso, Blind signal separation: statistical principles, Proceedings of the IEEE, pp.2009-2025, 1998.
DOI : 10.1109/5.720250

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

K. T. Herring, A. V. Mueller, and D. H. Staelin, Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.10, pp.3406-3415, 2009.
DOI : 10.1109/TGRS.2009.2022325

F. Ghaderi, H. R. Mohseni, and S. Sanei, A fast second order blind identification method for separation of periodic sources, 18th European Signal Processing Conference, pp.1572-1576, 2010.

L. Tong, V. C. Soon, Y. F. Huang, and R. Liu, Amuse: A new blind identification algorithm, Circuits and Systems, IEEE International Symposium, pp.1784-1787, 1990.