Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163523
Title: Community detection based process decomposition and distributed monitoring for large-scale processes
Authors: Yin, Xunyuan
Qin, Yan
Chen, Hongtian
Du, Wenli
Liu, Jinfeng
Huang, Biao
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: 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
Project: RS15/21
Journal: AIChE Journal
Abstract: 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.
URI: https://hdl.handle.net/10356/163523
ISSN: 0001-1541
DOI: 10.1002/aic.17826
Schools: School of Chemical and Biomedical Engineering 
School of Electrical and Electronic Engineering 
Rights: © 2022 American Institute of Chemical Engineers. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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