A Multivariate Homogeneously Weighted Moving Average Control Chart

This paper presents a multivariate homogeneously weighted moving average (MHWMA) control chart for monitoring a process mean vector. The MHWMA control chart statistic gives a specific weight to the current observation, and the remaining weight is evenly distributed among the previous observations. W...

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Main Authors: Nurudeen A. Adegoke, Saddam Akber Abbasi, Adam N. H. Smith, Marti J. Anderson, Matthew D. M. Pawley
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8606429/
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spelling doaj-f999186f772f47f9b7990368681d45222021-03-29T22:45:19ZengIEEEIEEE Access2169-35362019-01-0179586959710.1109/ACCESS.2019.28919888606429A Multivariate Homogeneously Weighted Moving Average Control ChartNurudeen A. Adegoke0https://orcid.org/0000-0001-7592-5460Saddam Akber Abbasi1https://orcid.org/0000-0003-1843-8863Adam N. H. Smith2Marti J. Anderson3Matthew D. M. Pawley4School of Natural and Computational Sciences, Massey University at Albany, Auckland, New ZealandDepartment of Mathematics, Statistics, and Physics, Qatar University, Doha, QatarSchool of Natural and Computational Sciences, Massey University at Albany, Auckland, New ZealandNew Zealand Institute for Advanced Study, Massey University at Albany, Auckland, New ZealandSchool of Natural and Computational Sciences, Massey University at Albany, Auckland, New ZealandThis paper presents a multivariate homogeneously weighted moving average (MHWMA) control chart for monitoring a process mean vector. The MHWMA control chart statistic gives a specific weight to the current observation, and the remaining weight is evenly distributed among the previous observations. We present the design procedure and compare the average run length (ARL) performance of the proposed chart with multivariate Chi-square, multivariate EWMA, and multivariate cumulative sum control charts. The ARL comparison indicates superior performance of the MHWMA chart over its competitors, particularly for the detection of small shifts in the process mean vector. Examples are also provided to show the application of the proposed chart.https://ieeexplore.ieee.org/document/8606429/Average run lengthcontrol chartmanufacturing processquality controlstatistical process control
collection DOAJ
language English
format Article
sources DOAJ
author Nurudeen A. Adegoke
Saddam Akber Abbasi
Adam N. H. Smith
Marti J. Anderson
Matthew D. M. Pawley
spellingShingle Nurudeen A. Adegoke
Saddam Akber Abbasi
Adam N. H. Smith
Marti J. Anderson
Matthew D. M. Pawley
A Multivariate Homogeneously Weighted Moving Average Control Chart
IEEE Access
Average run length
control chart
manufacturing process
quality control
statistical process control
author_facet Nurudeen A. Adegoke
Saddam Akber Abbasi
Adam N. H. Smith
Marti J. Anderson
Matthew D. M. Pawley
author_sort Nurudeen A. Adegoke
title A Multivariate Homogeneously Weighted Moving Average Control Chart
title_short A Multivariate Homogeneously Weighted Moving Average Control Chart
title_full A Multivariate Homogeneously Weighted Moving Average Control Chart
title_fullStr A Multivariate Homogeneously Weighted Moving Average Control Chart
title_full_unstemmed A Multivariate Homogeneously Weighted Moving Average Control Chart
title_sort multivariate homogeneously weighted moving average control chart
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description This paper presents a multivariate homogeneously weighted moving average (MHWMA) control chart for monitoring a process mean vector. The MHWMA control chart statistic gives a specific weight to the current observation, and the remaining weight is evenly distributed among the previous observations. We present the design procedure and compare the average run length (ARL) performance of the proposed chart with multivariate Chi-square, multivariate EWMA, and multivariate cumulative sum control charts. The ARL comparison indicates superior performance of the MHWMA chart over its competitors, particularly for the detection of small shifts in the process mean vector. Examples are also provided to show the application of the proposed chart.
topic Average run length
control chart
manufacturing process
quality control
statistical process control
url https://ieeexplore.ieee.org/document/8606429/
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