Flow pattern identification of gas-liquid flow based on the hybrid model of multi-scale information entropy feature and LS-SVM

Based on the characteristic that the Empirical Mode Decomposition (EMD) can decompose signal adaptively, a flow pattern identification method based on EMD multi-scale information entropy was put forward. Firstly, the acquired pressure-difference fluctuation signals are decomposed through EMD, and th...

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Bibliographic Details
Published in2008 7th World Congress on Intelligent Control and Automation pp. 8339 - 8344
Main Authors Wenzhe Qi, Kanxuan Wu, Zhenrui Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Adaptation model
Artificial neural networks
Empirical Mode Decomposition (EMD)
Entropy
flow-pattern
Fluctuations
Information entropy
Least Squares Support Vector Machine (LS-SVM)
Support vector machines
Time series analysis
two-phase flow
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Summary:Based on the characteristic that the Empirical Mode Decomposition (EMD) can decompose signal adaptively, a flow pattern identification method based on EMD multi-scale information entropy was put forward. Firstly, the acquired pressure-difference fluctuation signals are decomposed through EMD, and the decomposed signals within different frequency bands are obtained adaptively. Secondly, the multi-scale information signal entropy eigenvectors of flow pattern are abstracted. Finally, those eigenvectors are fed into the established hybrid model of EMD and LS-SVM for flow pattern identification and thus the flow pattern intelligent identification is realized. The experimental results show that this method can precisely identify the flow patterns of bubble flow, plug flow, and churn flow, respectively.
ISBN:1424421136
9781424421138
DOI:10.1109/WCICA.2008.4594235