New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/16/3566 |
| _version_ | 1797583949208223744 |
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| author | Hamed Sabahno Seyed Taghi Akhavan Niaki |
| author_facet | Hamed Sabahno Seyed Taghi Akhavan Niaki |
| author_sort | Hamed Sabahno |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:45:21Z |
| format | Article |
| id | doaj.art-2e1c627d282e4b0b9244e13805c108e0 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:45:21Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2e1c627d282e4b0b9244e13805c108e02023-11-19T02:03:53ZengMDPI AGMathematics2227-73902023-08-011116356610.3390/math11163566New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and IdentificationHamed Sabahno0Seyed Taghi Akhavan Niaki1Department of Statistics, School of Business, Economics and Statistics, Umeå University, Umeå 901 87, SwedenDepartment of Industrial Engineering, Sharif University of Technology, Tehran 1458889694, IranSimultaneous 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.https://www.mdpi.com/2227-7390/11/16/3566process monitoringmachine-learning techniquessimultaneous process parameters monitoringmultivariate normal processsimulation |
| spellingShingle | Hamed Sabahno Seyed Taghi Akhavan Niaki New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification Mathematics process monitoring machine-learning techniques simultaneous process parameters monitoring multivariate normal process simulation |
| title | New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification |
| title_full | New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification |
| title_fullStr | New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification |
| title_full_unstemmed | New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification |
| title_short | New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification |
| title_sort | new machine learning control charts for simultaneous monitoring of multivariate normal process parameters with detection and identification |
| topic | process monitoring machine-learning techniques simultaneous process parameters monitoring multivariate normal process simulation |
| url | https://www.mdpi.com/2227-7390/11/16/3566 |
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