High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates
The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new proc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9888137/ |
| _version_ | 1798031504766402560 |
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| author | Sangahn Kim Mehmet Turkoz Jung Woo Baek |
| author_facet | Sangahn Kim Mehmet Turkoz Jung Woo Baek |
| author_sort | Sangahn Kim |
| collection | DOAJ |
| description | The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new process monitoring scheme in a Bayesian framework. The key strategy of this study is to incorporate sequential observations into the estimation procedure for the parameters of interest to update the prior distribution. Based on the updated prior, we obtain the most appropriate estimation of the process parameters at each sampling epoch by maximizing the posterior probability. In addition, conventional statistical process control and monitoring methodologies suffer from the “curse of dimensionality.” The closed form of the estimate developed in this study through Bayesian updates enables the proposed method to be effective for high-dimensional process monitoring. Various simulation studies demonstrate the superiority of the proposed scheme in the high-speed monitoring of high-dimensional processes. Moreover, a few sample paths of the estimated mean in a procedure of the proposed method are illustrated to provide practitioners with insights into the monitoring and control of the process. Finally, we provide a real-life application to illustrate the proposed method. |
| first_indexed | 2024-04-11T19:58:34Z |
| format | Article |
| id | doaj.art-e0c97ecc8f2c444ea1f12401d9aeb564 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T19:58:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e0c97ecc8f2c444ea1f12401d9aeb5642022-12-22T04:05:53ZengIEEEIEEE Access2169-35362022-01-0110974509746410.1109/ACCESS.2022.32063699888137High-Speed Monitoring of Multidimensional Processes Using Bayesian UpdatesSangahn Kim0Mehmet Turkoz1Jung Woo Baek2https://orcid.org/0000-0002-4695-8738Department of Business Analytics and Actuarial Science, Siena College, Loudonville, NY, USADepartment of Management, Marketing and Professional Sales, William Paterson University, Wayne, NJ, USADepartment of Industrial Engineering, Chosun University, Gwangju, South KoreaThe advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new process monitoring scheme in a Bayesian framework. The key strategy of this study is to incorporate sequential observations into the estimation procedure for the parameters of interest to update the prior distribution. Based on the updated prior, we obtain the most appropriate estimation of the process parameters at each sampling epoch by maximizing the posterior probability. In addition, conventional statistical process control and monitoring methodologies suffer from the “curse of dimensionality.” The closed form of the estimate developed in this study through Bayesian updates enables the proposed method to be effective for high-dimensional process monitoring. Various simulation studies demonstrate the superiority of the proposed scheme in the high-speed monitoring of high-dimensional processes. Moreover, a few sample paths of the estimated mean in a procedure of the proposed method are illustrated to provide practitioners with insights into the monitoring and control of the process. Finally, we provide a real-life application to illustrate the proposed method.https://ieeexplore.ieee.org/document/9888137/Autocorrelated processBayesian updatehigh-dimensional processprocess mean monitoringstatistical process control |
| spellingShingle | Sangahn Kim Mehmet Turkoz Jung Woo Baek High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates IEEE Access Autocorrelated process Bayesian update high-dimensional process process mean monitoring statistical process control |
| title | High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates |
| title_full | High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates |
| title_fullStr | High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates |
| title_full_unstemmed | High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates |
| title_short | High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates |
| title_sort | high speed monitoring of multidimensional processes using bayesian updates |
| topic | Autocorrelated process Bayesian update high-dimensional process process mean monitoring statistical process control |
| url | https://ieeexplore.ieee.org/document/9888137/ |
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