New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification
Sabahno, Hamed LU
and Niaki, Seyed Taghi Akhavan
(2023)
In Mathematics
11(16).
- Abstract
Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training... (More)
Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.
(Less)
- author
- Sabahno, Hamed
LU
and Niaki, Seyed Taghi Akhavan
- publishing date
- 2023-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- machine-learning techniques, multivariate normal process, process monitoring, simulation, simultaneous process parameters monitoring
- in
- Mathematics
- volume
- 11
- issue
- 16
- article number
- 3566
- publisher
- MDPI AG
- external identifiers
-
- scopus:85180270942
- ISSN
- 2227-7390
- DOI
- 10.3390/math11163566
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2023 by the authors.
- id
- ff04bdcb-8dbb-486a-b197-53b475c32bcb
- date added to LUP
- 2025-03-20 12:17:02
- date last changed
- 2025-04-04 14:35:05
@article{ff04bdcb-8dbb-486a-b197-53b475c32bcb,
abstract = {{<p>Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.</p>}},
author = {{Sabahno, Hamed and Niaki, Seyed Taghi Akhavan}},
issn = {{2227-7390}},
keywords = {{machine-learning techniques; multivariate normal process; process monitoring; simulation; simultaneous process parameters monitoring}},
language = {{eng}},
number = {{16}},
publisher = {{MDPI AG}},
series = {{Mathematics}},
title = {{New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification}},
url = {{http://dx.doi.org/10.3390/math11163566}},
doi = {{10.3390/math11163566}},
volume = {{11}},
year = {{2023}},
}