New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles
Sabahno, Hamed LU
and Amiri, Amirhossein
(2023)
In Computers and Industrial Engineering
184.
- Abstract
In this research, we develop three statistical based control charts: the Hotelling's T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an... (More)
In this research, we develop three statistical based control charts: the Hotelling's T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and time-to-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme. After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments. Finally, we present a numerical example regarding a drug dose-response study to show how the proposed control charts can be implemented in real practice.
(Less)
- author
- Sabahno, Hamed
LU
and Amiri, Amirhossein
- publishing date
- 2023-10
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Generalized linear models, Machine learning techniques, Monte Carlo simulation, Profile monitoring, Statistical process monitoring, Variable parameters control charts
- in
- Computers and Industrial Engineering
- volume
- 184
- article number
- 109562
- publisher
- Elsevier
- external identifiers
-
- scopus:85169978570
- ISSN
- 0360-8352
- DOI
- 10.1016/j.cie.2023.109562
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2023 The Author(s)
- id
- 399f340c-bc7b-41ee-8c02-c4b1a7845b3e
- date added to LUP
- 2025-03-20 12:22:21
- date last changed
- 2025-04-04 14:24:44
@article{399f340c-bc7b-41ee-8c02-c4b1a7845b3e,
abstract = {{<p>In this research, we develop three statistical based control charts: the Hotelling's T<sup>2</sup>, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and time-to-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme. After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments. Finally, we present a numerical example regarding a drug dose-response study to show how the proposed control charts can be implemented in real practice.</p>}},
author = {{Sabahno, Hamed and Amiri, Amirhossein}},
issn = {{0360-8352}},
keywords = {{Generalized linear models; Machine learning techniques; Monte Carlo simulation; Profile monitoring; Statistical process monitoring; Variable parameters control charts}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Computers and Industrial Engineering}},
title = {{New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles}},
url = {{http://dx.doi.org/10.1016/j.cie.2023.109562}},
doi = {{10.1016/j.cie.2023.109562}},
volume = {{184}},
year = {{2023}},
}