基于多元经验模态分解与卷积神经网络的气液两相流流型识别

doi:10.3969/j.issn.1000-1158.2023.01.11

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Abstract A flow pattern identification method of gas-liquid two-phase flow in vertical pipeline based on multiple empirical mode decomposition (MEMD) and convolution neural network (CNN) is proposed.Based on the measurement data collected by the digital electrical resistance tomography (ERT) system,MEMD analysis is carried out after preprocessing.By calculating the Pearson correlation coefficient between each component and the original signal,the eigenmode function (IMFs) is selected and the Hilbert marginal spectrum is solved.The standard deviation and mean value of Hilbert marginal spectrum are extracted as convolution neural network (CNN)input to identify the flow pattern.The results show that the method can effectively identify bubbly flow,slug flow and slug flow,and the average recognition accuracy can reach 96.43%.

Key words： metrology flow pattern recognition electrical resistance tomography multiple empirical mode decomposition convolutional neural network

Received: 11 October 2021 Published: 13 January 2023

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TB937

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	ZHANG Li-feng
	WANG Zhi

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ZHANG Li-feng,WANG Zhi. Flow Pattern Recognition Method of Gas-Liquid Two-Phase Flow Based on Multiple Empirical Mode Decomposition and Convolution Neural Network[J]. Acta Metrologica Sinica, 2023, 44(1): 73-79.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.01.11 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I1/73

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