article

Using neural networks to detect the bivariate process variance shifts pattern

Authors:

Chuen-Sheng Cheng,

Hui-Ping ChengAuthors Info & Claims

Computers and Industrial Engineering, Volume 60, Issue 2

Pages 269 - 278

https://doi.org/10.1016/j.cie.2010.11.009

Published: 01 March 2011 Publication History

Abstract

Most of the research in statistical process control has been focused on monitoring the process mean. Typically, it is also important to detect variance changes as well. This paper presents a neural network-based approach for detecting bivariate process variance shifts. Some important implementation issues of neural networks are investigated, including analysis window size, number of training examples, sample size, training algorithm, etc. The performance of the neural network, in terms of the ARL and run length distribution, is compared with that of traditional multivariate control charts. Through rigorous evaluation and comparison, our research results show that the proposed neural network performs substantially better than the traditional generalized variance chart and might perform better than the adaptive sizes control charts in the case that the out-of-control covariance matrix is not known in advance.

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Cited By

Yeganeh AShadman AShongwe SAbbasi S(2023)Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performanceNeural Computing and Applications10.1007/s00521-023-08257-x35:14(10677-10693)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1007/s00521-023-08257-x
He ZWang ZTsung FShang Y(2016)A control scheme for autocorrelated bivariate binomial dataComputers and Industrial Engineering10.1016/j.cie.2016.06.00198:C(350-359)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.06.001
Yang W(2016)Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based modelJournal of Intelligent Manufacturing10.1007/s10845-014-0920-927:4(845-874)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10845-014-0920-9
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Index Terms

Using neural networks to detect the bivariate process variance shifts pattern
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image Computers and Industrial Engineering

Computers and Industrial Engineering Volume 60, Issue 2

March, 2011

189 pages

ISSN:0360-8352

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2010.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2011

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Cited By

Yeganeh AShadman AShongwe SAbbasi S(2023)Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performanceNeural Computing and Applications10.1007/s00521-023-08257-x35:14(10677-10693)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1007/s00521-023-08257-x
He ZWang ZTsung FShang Y(2016)A control scheme for autocorrelated bivariate binomial dataComputers and Industrial Engineering10.1016/j.cie.2016.06.00198:C(350-359)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.06.001
Yang W(2016)Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based modelJournal of Intelligent Manufacturing10.1007/s10845-014-0920-927:4(845-874)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10845-014-0920-9
Khoo MWu ZCastagliola PLee H(2013)A multivariate synthetic double sampling T2 control chartComputers and Industrial Engineering10.1016/j.cie.2012.08.01764:1(179-189)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.cie.2012.08.017
Hachicha WGhorbel A(2012)A survey of control-chart pattern-recognition literature (1991-2010) based on a new conceptual classification schemeComputers and Industrial Engineering10.1016/j.cie.2012.03.00263:1(204-222)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.cie.2012.03.002

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