Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity
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 decompo...
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Published in | The Korean journal of chemical engineering Vol. 39; no. 2; pp. 275 - 283 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.02.2022
Springer Nature B.V 한국화학공학회 |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0256-1115 1975-7220 |
DOI: | 10.1007/s11814-021-0901-6 |