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

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