COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK

COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK

Computer integrated manufacturing environments and competition among companies to meet customer requirements raise the need for the use of online methodologies in combination with traditional Statistical Process Control tools. This study focuses on detecting the change point, when a shift in mean oc...

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Bibliographic Details
Main Author: Ghasemi, Mandana
Format: Others
Published: OpenSIUC 2014
Subjects:
Change Point
MCUSUM
MEWMA
Neural Network
Online Access:https://opensiuc.lib.siu.edu/theses/1589
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2603&context=theses
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spelling ndltd-siu.edu-oai-opensiuc.lib.siu.edu-theses-26032018-12-20T04:39:50Z COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK Ghasemi, Mandana Computer integrated manufacturing environments and competition among companies to meet customer requirements raise the need for the use of online methodologies in combination with traditional Statistical Process Control tools. This study focuses on detecting the change point, when a shift in mean occurs, in a normal bivariate process using two different approaches. First, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA) statistical procedures were used in detecting the mean shift in the process. Then the step-change detection and neural network approaches were applied to the outputs of MCUSUM and MEWMA statistical procedures to identify the time of the change. The results show that the step-change and neural network approaches are capable of detecting the time of the change earlier than either the MCUSUM or MEWMA statistical procedure. 2014-12-01T08:00:00Z text application/pdf https://opensiuc.lib.siu.edu/theses/1589 https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2603&context=theses Theses OpenSIUC Change Point MCUSUM MEWMA Neural Network
collection NDLTD
format Others
sources NDLTD
topic Change Point
MCUSUM
MEWMA
Neural Network
spellingShingle Change Point
MCUSUM
MEWMA
Neural Network
Ghasemi, Mandana
COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
description Computer integrated manufacturing environments and competition among companies to meet customer requirements raise the need for the use of online methodologies in combination with traditional Statistical Process Control tools. This study focuses on detecting the change point, when a shift in mean occurs, in a normal bivariate process using two different approaches. First, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA) statistical procedures were used in detecting the mean shift in the process. Then the step-change detection and neural network approaches were applied to the outputs of MCUSUM and MEWMA statistical procedures to identify the time of the change. The results show that the step-change and neural network approaches are capable of detecting the time of the change earlier than either the MCUSUM or MEWMA statistical procedure.
author Ghasemi, Mandana
author_facet Ghasemi, Mandana
author_sort Ghasemi, Mandana
title COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
title_short COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
title_full COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
title_fullStr COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
title_full_unstemmed COMPARISON OF MULTIVARIATE PROCESS MEAN SHIFT APPROACHES: MEWMA, MCUSUM, CHANGE POINT AND NEURAL NETWORK
title_sort comparison of multivariate process mean shift approaches: mewma, mcusum, change point and neural network
publisher OpenSIUC
publishDate 2014
url https://opensiuc.lib.siu.edu/theses/1589
https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2603&context=theses
work_keys_str_mv AT ghasemimandana comparisonofmultivariateprocessmeanshiftapproachesmewmamcusumchangepointandneuralnetwork
_version_ 1718803345406164992

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