Dynamic multivariate threshold optimization and alarming for nonstationary processes subject to varying conditions. (July 2022)
- Record Type:
- Journal Article
- Title:
- Dynamic multivariate threshold optimization and alarming for nonstationary processes subject to varying conditions. (July 2022)
- Main Title:
- Dynamic multivariate threshold optimization and alarming for nonstationary processes subject to varying conditions
- Authors:
- Zhao, Yi
Zhao, Chunhui - Abstract:
- Abstract: For the alarm system of nonstationary industrial processes, the conventional alarm thresholds configured for one single operational zone often result in frequent false and missed alarms. Besides, these univariate thresholds neglect interactions among process variables. To address these issues, this paper proposes a novel dynamic multivariate alarm threshold optimization algorithm for nonstationary processes subject to varying conditions. Firstly, the process correlation variations can be identified by Toeplitz inverse covariance-based clustering method, pointing to the changes of operating conditions. Each condition can be structurally interpreted by an inverse covariance matrix of the multivariate Gaussian distribution, revealing similar within-time and cross-time variable interactions. Therefore, it provides a promising foundation to capture the conditional Gaussian distribution and design the corresponding thresholds of each variable, which finely covers the current normal operational zone. Then, offline threshold optimization and online alarming strategy are developed and discussed in detail, which can timely adapt the model to varying conditions, promoting accurate and sensitive alarming performance. Finally, the validity of the proposed threshold is demonstrated on both continuous and batch processes with typical nonstationary characteristics. Results show that the proposed threshold can effectively adapt to varying conditions and aggregate multivariateAbstract: For the alarm system of nonstationary industrial processes, the conventional alarm thresholds configured for one single operational zone often result in frequent false and missed alarms. Besides, these univariate thresholds neglect interactions among process variables. To address these issues, this paper proposes a novel dynamic multivariate alarm threshold optimization algorithm for nonstationary processes subject to varying conditions. Firstly, the process correlation variations can be identified by Toeplitz inverse covariance-based clustering method, pointing to the changes of operating conditions. Each condition can be structurally interpreted by an inverse covariance matrix of the multivariate Gaussian distribution, revealing similar within-time and cross-time variable interactions. Therefore, it provides a promising foundation to capture the conditional Gaussian distribution and design the corresponding thresholds of each variable, which finely covers the current normal operational zone. Then, offline threshold optimization and online alarming strategy are developed and discussed in detail, which can timely adapt the model to varying conditions, promoting accurate and sensitive alarming performance. Finally, the validity of the proposed threshold is demonstrated on both continuous and batch processes with typical nonstationary characteristics. Results show that the proposed threshold can effectively adapt to varying conditions and aggregate multivariate information, thus reducing nuisances and ensuring the reliability of the alarm system as well. … (more)
- Is Part Of:
- Control engineering practice. Volume 124(2022)
- Journal:
- Control engineering practice
- Issue:
- Volume 124(2022)
- Issue Display:
- Volume 124, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 124
- Issue:
- 2022
- Issue Sort Value:
- 2022-0124-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07
- Subjects:
- Alarm system -- Dynamic threshold optimization -- Nonstationary process -- Varying conditions
Automatic control -- Periodicals
629.89 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09670661 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.conengprac.2022.105180 ↗
- Languages:
- English
- ISSNs:
- 0967-0661
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3462.020000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
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