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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received April 3, 2021
Accepted July 12, 2021

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity

Ruoyu Huang^{1 2} Zetao Li^1† Bin Cao³

¹The Electrical Engineering College, Guizhou University, Guiyang 550025, China ²Guiyang Aluminum Magnesium Design and Research Institute Co., Ltd., Guiyang 550081, China ³Chinalco Intelligent Technology Development Co., Ltd., Hangzhou 311199, China

ztli@gzu.edu.cn

Korean Journal of Chemical Engineering, February 2022, 39(2), 275-283(9), 10.1007/s11814-021-0901-6

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Abstract

In order to overcome the dynamic and large-scale characteristics of the plant-wide processes, this paper proposed a distributed slow feature analysis (SFA) with inter-unit dissimilarity method for process monitoring task. Firstly, to highlight the local dynamic features, the whole process is decomposed into several units according to the prior knowledge. Based on this, SFA monitoring model is built parallelly to handle the dynamic features. Considering the possible information loss caused by the process decomposition, the inter-unit dissimilarity index is carried out to monitor the variations between adjacent units. Finally, the fusion center is conducted by Bayesian inference to combine the results of SFA monitoring models and inter-unit dissimilarity statistics. The effectiveness of the proposed method is tested on the Tennessee Eastman process and an aluminum electrolysis process.

Keywords

Distributed Monitoring Slow Feature Analysis Dynamic Process Fault Detection Inter-unit Dissimilarity

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