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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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/ |
work_keys_str_mv |
AT nurudeenaadegoke amultivariatehomogeneouslyweightedmovingaveragecontrolchart AT saddamakberabbasi amultivariatehomogeneouslyweightedmovingaveragecontrolchart AT adamnhsmith amultivariatehomogeneouslyweightedmovingaveragecontrolchart AT martijanderson amultivariatehomogeneouslyweightedmovingaveragecontrolchart AT matthewdmpawley amultivariatehomogeneouslyweightedmovingaveragecontrolchart AT nurudeenaadegoke multivariatehomogeneouslyweightedmovingaveragecontrolchart AT saddamakberabbasi multivariatehomogeneouslyweightedmovingaveragecontrolchart AT adamnhsmith multivariatehomogeneouslyweightedmovingaveragecontrolchart AT martijanderson multivariatehomogeneouslyweightedmovingaveragecontrolchart AT matthewdmpawley multivariatehomogeneouslyweightedmovingaveragecontrolchart |
_version_ |
1724190981714083840 |