Open Access
2014 Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches
Yuehjen E. Shao
Abstr. Appl. Anal. 2014(SI11): 1-9 (2014). DOI: 10.1155/2014/519705

Abstract

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.

Citation

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Yuehjen E. Shao. "Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches." Abstr. Appl. Anal. 2014 (SI11) 1 - 9, 2014. https://doi.org/10.1155/2014/519705

Information

Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07022542
Digital Object Identifier: 10.1155/2014/519705

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI11 • 2014
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