A Review of Change Point Estimation Methods for Process Monitoring

Ademola John Ogunniran; Kayode Samuel Adekeye; Johnson Ademola Adewara; Muminu Adamu

doi:doi:10.11648/j.acm.20211003.13

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A Review of Change Point Estimation Methods for Process Monitoring

Ademola John Ogunniran, Kayode Samuel Adekeye, Johnson Ademola Adewara, Muminu Adamu

Published in Applied and Computational Mathematics (Volume 10, Issue 3)

Received: 15 March 2021 Accepted: 7 April 2021 Published: 29 June 2021

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Abstract

When one or more observations fall outside the control limits, the chart signals the existence of a change in the process. Change point detection is helpful in modelling and prediction of time series and is found in broader areas of applications including process monitoring. Three approaches were proposed for estimating change point in process for the different types of changes in the literature. they are: Maximum Likelihood Estimator (MLE), the Cumulative Sum (CUSUM), and the Exponentially Weighted Moving Average (EWMA) approaches. This paper gives a synopsis of change point estimation, specifies, categorizes, and evaluates many of the methods that have been recommended for detecting change points in process monitoring. The change points articles in the literature were categorized broadly under five categories, namely: types of process, types of data, types of change, types of phase and methods of estimation. Aside the five broad categories, we also included the parameter involved. Furthermore, the use of control charts and other monitoring tools used to detect abrupt changes in processes were reviewed and the gaps for process monitoring/controlling were examined. A combination of different methods of estimation will be a valuable approach to finding the best estimates of change point models. Further research studies would include assessing the sensitivity of the various change point estimators to deviations in the underlying distributional assumptions.

Published in	Applied and Computational Mathematics (Volume 10, Issue 3)
DOI	10.11648/j.acm.20211003.13
Page(s)	69-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Change Points, Control Charts, Estimation, Process Monitoring, Gaps

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APA Style

Ademola John Ogunniran, Kayode Samuel Adekeye, Johnson Ademola Adewara, Muminu Adamu. (2021). A Review of Change Point Estimation Methods for Process Monitoring. Applied and Computational Mathematics, 10(3), 69-75. https://doi.org/10.11648/j.acm.20211003.13

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ACS Style

Ademola John Ogunniran; Kayode Samuel Adekeye; Johnson Ademola Adewara; Muminu Adamu. A Review of Change Point Estimation Methods for Process Monitoring. Appl. Comput. Math. 2021, 10(3), 69-75. doi: 10.11648/j.acm.20211003.13

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AMA Style

Ademola John Ogunniran, Kayode Samuel Adekeye, Johnson Ademola Adewara, Muminu Adamu. A Review of Change Point Estimation Methods for Process Monitoring. Appl Comput Math. 2021;10(3):69-75. doi: 10.11648/j.acm.20211003.13

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@article{10.11648/j.acm.20211003.13,
  author = {Ademola John Ogunniran and Kayode Samuel Adekeye and Johnson Ademola Adewara and Muminu Adamu},
  title = {A Review of Change Point Estimation Methods for Process Monitoring},
  journal = {Applied and Computational Mathematics},
  volume = {10},
  number = {3},
  pages = {69-75},
  doi = {10.11648/j.acm.20211003.13},
  url = {https://doi.org/10.11648/j.acm.20211003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20211003.13},
  abstract = {When one or more observations fall outside the control limits, the chart signals the existence of a change in the process. Change point detection is helpful in modelling and prediction of time series and is found in broader areas of applications including process monitoring. Three approaches were proposed for estimating change point in process for the different types of changes in the literature. they are: Maximum Likelihood Estimator (MLE), the Cumulative Sum (CUSUM), and the Exponentially Weighted Moving Average (EWMA) approaches. This paper gives a synopsis of change point estimation, specifies, categorizes, and evaluates many of the methods that have been recommended for detecting change points in process monitoring. The change points articles in the literature were categorized broadly under five categories, namely: types of process, types of data, types of change, types of phase and methods of estimation. Aside the five broad categories, we also included the parameter involved. Furthermore, the use of control charts and other monitoring tools used to detect abrupt changes in processes were reviewed and the gaps for process monitoring/controlling were examined. A combination of different methods of estimation will be a valuable approach to finding the best estimates of change point models. Further research studies would include assessing the sensitivity of the various change point estimators to deviations in the underlying distributional assumptions.},
 year = {2021}
}

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TY  - JOUR
T1  - A Review of Change Point Estimation Methods for Process Monitoring
AU  - Ademola John Ogunniran
AU  - Kayode Samuel Adekeye
AU  - Johnson Ademola Adewara
AU  - Muminu Adamu
Y1  - 2021/06/29
PY  - 2021
N1  - https://doi.org/10.11648/j.acm.20211003.13
DO  - 10.11648/j.acm.20211003.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 69
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20211003.13
AB  - When one or more observations fall outside the control limits, the chart signals the existence of a change in the process. Change point detection is helpful in modelling and prediction of time series and is found in broader areas of applications including process monitoring. Three approaches were proposed for estimating change point in process for the different types of changes in the literature. they are: Maximum Likelihood Estimator (MLE), the Cumulative Sum (CUSUM), and the Exponentially Weighted Moving Average (EWMA) approaches. This paper gives a synopsis of change point estimation, specifies, categorizes, and evaluates many of the methods that have been recommended for detecting change points in process monitoring. The change points articles in the literature were categorized broadly under five categories, namely: types of process, types of data, types of change, types of phase and methods of estimation. Aside the five broad categories, we also included the parameter involved. Furthermore, the use of control charts and other monitoring tools used to detect abrupt changes in processes were reviewed and the gaps for process monitoring/controlling were examined. A combination of different methods of estimation will be a valuable approach to finding the best estimates of change point models. Further research studies would include assessing the sensitivity of the various change point estimators to deviations in the underlying distributional assumptions.
VL  - 10
IS  - 3
ER  -

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Author Information

Ademola John Ogunniran

Department of Mathematics, University of Lagos, Akoka, Lagos, Nigeria
Kayode Samuel Adekeye

Department of Mathematical Sciences, Redeemer’s University, Ede, Nigeria
Johnson Ademola Adewara

Distance Learning Institute, University of Lagos, Akoka, Lagos, Nigeria
Muminu Adamu

Department of Mathematics, University of Lagos, Akoka, Lagos, Nigeria

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APA Style

Ademola John Ogunniran, Kayode Samuel Adekeye, Johnson Ademola Adewara, Muminu Adamu. (2021). A Review of Change Point Estimation Methods for Process Monitoring. Applied and Computational Mathematics, 10(3), 69-75. https://doi.org/10.11648/j.acm.20211003.13

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ACS Style

Ademola John Ogunniran; Kayode Samuel Adekeye; Johnson Ademola Adewara; Muminu Adamu. A Review of Change Point Estimation Methods for Process Monitoring. Appl. Comput. Math. 2021, 10(3), 69-75. doi: 10.11648/j.acm.20211003.13

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AMA Style

Ademola John Ogunniran, Kayode Samuel Adekeye, Johnson Ademola Adewara, Muminu Adamu. A Review of Change Point Estimation Methods for Process Monitoring. Appl Comput Math. 2021;10(3):69-75. doi: 10.11648/j.acm.20211003.13

Copy | Download

@article{10.11648/j.acm.20211003.13,
  author = {Ademola John Ogunniran and Kayode Samuel Adekeye and Johnson Ademola Adewara and Muminu Adamu},
  title = {A Review of Change Point Estimation Methods for Process Monitoring},
  journal = {Applied and Computational Mathematics},
  volume = {10},
  number = {3},
  pages = {69-75},
  doi = {10.11648/j.acm.20211003.13},
  url = {https://doi.org/10.11648/j.acm.20211003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20211003.13},
  abstract = {When one or more observations fall outside the control limits, the chart signals the existence of a change in the process. Change point detection is helpful in modelling and prediction of time series and is found in broader areas of applications including process monitoring. Three approaches were proposed for estimating change point in process for the different types of changes in the literature. they are: Maximum Likelihood Estimator (MLE), the Cumulative Sum (CUSUM), and the Exponentially Weighted Moving Average (EWMA) approaches. This paper gives a synopsis of change point estimation, specifies, categorizes, and evaluates many of the methods that have been recommended for detecting change points in process monitoring. The change points articles in the literature were categorized broadly under five categories, namely: types of process, types of data, types of change, types of phase and methods of estimation. Aside the five broad categories, we also included the parameter involved. Furthermore, the use of control charts and other monitoring tools used to detect abrupt changes in processes were reviewed and the gaps for process monitoring/controlling were examined. A combination of different methods of estimation will be a valuable approach to finding the best estimates of change point models. Further research studies would include assessing the sensitivity of the various change point estimators to deviations in the underlying distributional assumptions.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - A Review of Change Point Estimation Methods for Process Monitoring
AU  - Ademola John Ogunniran
AU  - Kayode Samuel Adekeye
AU  - Johnson Ademola Adewara
AU  - Muminu Adamu
Y1  - 2021/06/29
PY  - 2021
N1  - https://doi.org/10.11648/j.acm.20211003.13
DO  - 10.11648/j.acm.20211003.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 69
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20211003.13
AB  - When one or more observations fall outside the control limits, the chart signals the existence of a change in the process. Change point detection is helpful in modelling and prediction of time series and is found in broader areas of applications including process monitoring. Three approaches were proposed for estimating change point in process for the different types of changes in the literature. they are: Maximum Likelihood Estimator (MLE), the Cumulative Sum (CUSUM), and the Exponentially Weighted Moving Average (EWMA) approaches. This paper gives a synopsis of change point estimation, specifies, categorizes, and evaluates many of the methods that have been recommended for detecting change points in process monitoring. The change points articles in the literature were categorized broadly under five categories, namely: types of process, types of data, types of change, types of phase and methods of estimation. Aside the five broad categories, we also included the parameter involved. Furthermore, the use of control charts and other monitoring tools used to detect abrupt changes in processes were reviewed and the gaps for process monitoring/controlling were examined. A combination of different methods of estimation will be a valuable approach to finding the best estimates of change point models. Further research studies would include assessing the sensitivity of the various change point estimators to deviations in the underlying distributional assumptions.
VL  - 10
IS  - 3
ER  -

Copy | Download