Community detection based process decomposition and distributed monitoring for large-scale processes
Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distri...
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sg-ntu-dr.10356-1635232022-12-08T03:54:49Z Community detection based process decomposition and distributed monitoring for large-scale processes Yin, Xunyuan Qin, Yan Chen, Hongtian Du, Wenli Liu, Jinfeng Huang, Biao School of Chemical and Biomedical Engineering School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Community Structure Detection Distributed Process Monitoring Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process. Ministry of Education (MOE) Nanyang Technological University This research is supported by Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RS15/21), Nanyang Technological University, Singapore (Start-Up Grant), National Natural Science Foundation of China (Basic Science Center Program) (61988101), and Natural Sciences and Engineering Research Council of Canada. 2022-12-08T03:54:49Z 2022-12-08T03:54:49Z 2022 Journal Article Yin, X., Qin, Y., Chen, H., Du, W., Liu, J. & Huang, B. (2022). Community detection based process decomposition and distributed monitoring for large-scale processes. AIChE Journal, 68(11), e17826-. https://dx.doi.org/10.1002/aic.17826 0001-1541 https://hdl.handle.net/10356/163523 10.1002/aic.17826 2-s2.0-85134200774 11 68 e17826 en RS15/21 AIChE Journal © 2022 American Institute of Chemical Engineers. All rights reserved. |
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Engineering::Electrical and electronic engineering Community Structure Detection Distributed Process Monitoring Yin, Xunyuan Qin, Yan Chen, Hongtian Du, Wenli Liu, Jinfeng Huang, Biao Community detection based process decomposition and distributed monitoring for large-scale processes |
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Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Yin, Xunyuan Qin, Yan Chen, Hongtian Du, Wenli Liu, Jinfeng Huang, Biao |
format |
Article |
author |
Yin, Xunyuan Qin, Yan Chen, Hongtian Du, Wenli Liu, Jinfeng Huang, Biao |
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Yin, Xunyuan |
title |
Community detection based process decomposition and distributed monitoring for large-scale processes |
title_short |
Community detection based process decomposition and distributed monitoring for large-scale processes |
title_full |
Community detection based process decomposition and distributed monitoring for large-scale processes |
title_fullStr |
Community detection based process decomposition and distributed monitoring for large-scale processes |
title_full_unstemmed |
Community detection based process decomposition and distributed monitoring for large-scale processes |
title_sort |
community detection based process decomposition and distributed monitoring for large-scale processes |
publishDate |
2022 |
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https://hdl.handle.net/10356/163523 |
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1753801135648407552 |