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A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks
Faculty
Science
Year:
2010
Type of Publication:
Article
Pages:
1035-1042
Authors:
El-Midany, T. T, El-Baz, M. A, Abd-Elwahed, M. S
DOI:
10.1016/j.eswa.2009.05.092
Journal:
EXPERT SYSTEMS WITH APPLICATIONS PERGAMON-ELSEVIER SCIENCE LTD
Volume:
37
Research Area:
Computer Science; Engineering; Operations Research \& Management Science
ISSN
ISI:000272432300016
Keywords :
Multivariate statistical process control, Multivariate control charts, Pattern recognition, Artificial neural networks
Abstract:
This paper describes a proposed framework for multivariate process control chart recognition. The proposed methodology uses the Artificial Neural Networks (ANNs) to recognize set of subclasses of multivariate abnormal patterns. identify the responsible variable(s) oil the occurrence of abnormal pattern and classify the abnormal pattern parameters. The performance of the proposed approach has been evaluated using a real case study. The numerical and graphical results are presented which demonstrate that the approach performs effectively in control chart multivariate pattern recognition. In addition. accurately identifies and classifies the parameters of the errant variable(s). (C) 2009 Published by Elsevier Ltd
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