Application of a neural network to classify the out-of-control signal given by the multivariate Generalized Variance |S| control chart using real industry data.

Avendano, Gerardo

doi:10.46254/SA01.20170060

Quality Control and Management

Application of a neural network to classify the out-of-control signal given by the multivariate Generalized Variance |S| control chart using real industry data.

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Gerardo Avendano Universidad EAN

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Quality control techniques in the industry are used to detect changes in process dispersion, but it is necessary to monitor two or more quality variables simultaneously. The problems of process monitoring where several related variables are studied can be controlled by means of multivariate control charts. The objective of this work is to describe the implementation of the multivariate. Generalized Variance |S| control chart using real data obtained from the industry.

In the present research work the implementation of the multivariate Generalized Variance |S| control chart in the industrial field is carried out and a real multivariate case is analyzed with quality variables.

Published in 1st South American International Conference on Industrial Engineering and Operations Management, Bogota, Colombia

Publisher IEOM Society International

Date of Conferences October 25–26, 2017

ISBN 978-1-5323-5943-9

ISSN/E-ISSN 2169-8767

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Application of a neural network to classify the out-of-control signal given by the multivariate Generalized Variance |S| control chart using real industry data.