Developing Machine Learning-based Control Charts for Monitoring Different GLM-type Profiles With Different Link Functions
Hric, Patrik and Sabahno, Hamed LU
(2024)
In Applied Artificial Intelligence
38(1).
- Abstract
In certain situations, the quality of a process is determined by dependent variables in relation to independent variables, often modeled through a regression framework referred to as a profile. The practice of monitoring and preserving this relationship is known as profile monitoring. In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for constructing control charts and monitoring generalized linear model (GLM) profiles with three different GLM-type response distributions of Binomial, Poisson, and Gamma, and by examining different link functions for each response distribution. Through our simulation study, we undertake a comparative analysis of different training methods. We measure the... (More)
In certain situations, the quality of a process is determined by dependent variables in relation to independent variables, often modeled through a regression framework referred to as a profile. The practice of monitoring and preserving this relationship is known as profile monitoring. In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for constructing control charts and monitoring generalized linear model (GLM) profiles with three different GLM-type response distributions of Binomial, Poisson, and Gamma, and by examining different link functions for each response distribution. Through our simulation study, we undertake a comparative analysis of different training methods. We measure the charts’ performance using the average run length, which signifies the average number of samples taken before observing a data point that exceeds the predefined control limits. The result shows that the selection of ML control charts is contingent on the response distribution and link function, and depends on the shift sizes in the process and the utilized training method. To illustrate the practical application of the proposed ML control charts, we present two real-world cases as examples: a drug–response study and a volcano-eruption study, to demonstrate how each ML chart can be implemented in practice.
(Less)
- author
- Hric, Patrik
and Sabahno, Hamed
LU
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Applied Artificial Intelligence
- volume
- 38
- issue
- 1
- article number
- 2362511
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85195397124
- ISSN
- 0883-9514
- DOI
- 10.1080/08839514.2024.2362511
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
- id
- e7038711-e321-4ce9-bf31-7bfd055bb73a
- date added to LUP
- 2025-03-20 12:21:36
- date last changed
- 2025-04-04 14:44:24
@article{e7038711-e321-4ce9-bf31-7bfd055bb73a,
abstract = {{<p>In certain situations, the quality of a process is determined by dependent variables in relation to independent variables, often modeled through a regression framework referred to as a profile. The practice of monitoring and preserving this relationship is known as profile monitoring. In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for constructing control charts and monitoring generalized linear model (GLM) profiles with three different GLM-type response distributions of Binomial, Poisson, and Gamma, and by examining different link functions for each response distribution. Through our simulation study, we undertake a comparative analysis of different training methods. We measure the charts’ performance using the average run length, which signifies the average number of samples taken before observing a data point that exceeds the predefined control limits. The result shows that the selection of ML control charts is contingent on the response distribution and link function, and depends on the shift sizes in the process and the utilized training method. To illustrate the practical application of the proposed ML control charts, we present two real-world cases as examples: a drug–response study and a volcano-eruption study, to demonstrate how each ML chart can be implemented in practice.</p>}},
author = {{Hric, Patrik and Sabahno, Hamed}},
issn = {{0883-9514}},
language = {{eng}},
number = {{1}},
publisher = {{Taylor & Francis}},
series = {{Applied Artificial Intelligence}},
title = {{Developing Machine Learning-based Control Charts for Monitoring Different GLM-type Profiles With Different Link Functions}},
url = {{http://dx.doi.org/10.1080/08839514.2024.2362511}},
doi = {{10.1080/08839514.2024.2362511}},
volume = {{38}},
year = {{2024}},
}