Source(s) Identification of Variance Shifts in Bivariate Process Using LS-SVM Based Pattern Recognition Model

Abstract:

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MSPC techniques are effective tools for detecting the abnormalities of process variation. But MSPC charts do not provide the necessary information about which process variables (or subset of them) are responsible for the signal. In order to identify the process abnormality in covariance matrix of bivariate process, this article proposes a model based on LS-SVM pattern recognizer and |S| chart method, the main property of this model is to identify the assignable causes through LS-SVM pattern recognizer technique when |S| chart issue a warning signal. The simulation results indicate that the proposed model is feasible and effective. A bivariate example is presented to illustrate the application of the proposed model.

Info:

Periodical:

Advanced Materials Research (Volumes 129-131)

Edited by:

Xie Yi and Li Mi

Pages:

891-896

DOI:

10.4028/www.scientific.net/AMR.129-131.891

Citation:

Z. Q. Cheng and Y. Z. Ma, "Source(s) Identification of Variance Shifts in Bivariate Process Using LS-SVM Based Pattern Recognition Model", Advanced Materials Research, Vols. 129-131, pp. 891-896, 2010

Online since:

August 2010

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Price:

$35.00

[1] D.C. Montgomery: Introduction to Statistical Quality Control (Wiley, New York, 2004).

[2] H. Hotelling: Multivariate quality control (McGraw-Hill, New York, 1947).

[3] R.B. Crosier: Multivariate generalizations of cumulative sum quality control schemes [J], Technometrics, Vol. 30 (1988), pp.291-303.

DOI: 10.2307/1270083

[4] C.A. Lowry, W.H. Woodall, C.W. Champ, S.E. Rigdon: A multivariate exponentially weighted moving average control chart [J], Technometrics, Vol. 34 (1992), pp.46-53.

DOI: 10.2307/1269551

[5] F.B. Alt: Multivariate quality control (Wiley, New York, 1985).

[6] M.A.G. Machado, A.F.B. Costa, M.A. Rahim: The Synthetic Control Chart Based on Two Sample Variances for Monitoring the Covariance Matrix [J], Qual. Reliab. Engng. Int., Vol. 25 (5) (2008), pp.595-605.

DOI: 10.1002/qre.992

[7] Jian-bo Yu , Li-feng Xi: A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes [J], Expert Systems with Applications, Vol. 36(2009), pp.909-921.

DOI: 10.1016/j.eswa.2007.10.003

[8] N. Cristianini, J.S. Taylor: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [M] (Cambridge University Press, 2005).

DOI: 10.1017/cbo9780511801389.001

[9] J.A. K Suykens, J. Vandewalle: Least Squares Support Vector Machine Classifiers [J], Neural Processing Letters, Vol. 9 (1999), pp.293-300.

[10] Bo Liu, Zhifeng Hao, Xiaowei Yang: Nesting algorithm for multi-classification problems [J], Soft Computing, Vol. 11 (2007), pp.383-389.

DOI: 10.1007/s00500-006-0093-3

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