基于深度残差神经网络的电阻层析成像及流型辨识方法

doi:10.16182/j.issn1004731x.joss.21-0294

摘要/Abstract

摘要：

针对电阻层析成像(electrical resistance tomography, ERT)反问题成像精度和流型识别准确率偏低的问题，提出一种基于深度残差神经网络的两相流电阻层析成像及流型识别方法。利用有限元法对ERT正问题建模，构造多种气液两相流分布状态的“边界电压–电导率分布–流型类别”数据集。搭建用于气液两相流ERT图像重建和流型辨识的残差神经网络模型并进行网络训练，将残差神经网络的两个输出分别进行数据处理，得到重建的电导率分布图像和流型辨识结果。仿真与静态实验结果表明：该方法能够同时实现成像及流型辨识的需求，具有重建图像精度高、泛化性和抗噪性强、流型辨识准确度高的特点。

关键词: 残差神经网络, 深度学习, 电阻层析成像, 图像重建, 流型辨识

Abstract:

Aiming at the low accuracy of inverse problem imaging and flow pattern recognition in electrical resistance tomography (ERT), a two-phase flow electrical resistance tomography and flow pattern recognition method based on the deep residual neural network is proposed. The finite element method is used to model the ERT forward problem to construct the "boundary voltage-conductivity distribution-flow pattern category" dataset of various gas-liquid two-phase flow distributions. The residual neural network for ERT image reconstruction and flow pattern identification of gas-liquid two-phase flow is built and trained. The two outputs of the residual neural network are processed respectively to obtain the reconstructed conductivity distribution images and flow pattern identification results. The simulation and static experimental results show that the method can achieve the requirements of imaging and flow pattern identification simultaneously, and has the characteristics of high precision of reconstructed images. And the model is strong generalization and robustness to noise, and has the high accuracy of flow pattern identification.

Key words: residual neural network, deep learning, electrical resistance tomography (ERT), image reconstruction, flow pattern identification

中图分类号:

TP274
TP391

仝卫国, 曾世超, 张立峰, 侯哲, 郭佳跃. 基于深度残差神经网络的电阻层析成像及流型辨识方法[J]. 系统仿真学报, 2022, 34(09): 2028-2036.

Weiguo Tong, Shichao Zeng, Lifeng Zhang, Zhe Hou, Jiayue Guo. Electrical Resistance Tomography and Flow Pattern Identification Method Based on Deep Residual Neural Network[J]. Journal of System Simulation, 2022, 34(09): 2028-2036.

图/表 14

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算法	ICC	RIE
Landweber	0.608	0.801
FCDN	0.927	0.319
Vgg16	0.967	0.219
h-Net	0.972	0.202

算法	ICC	RIE
Landweber	0.526	1.249
FCDN	0.835	0.504
Vgg16	0.793	0.533
h-Net	0.894	0.379

流型	样本数	正确数	Acc/%
环流	480	479	99.8
单泡流	495	495	100
双泡流	509	509	100
三泡流	511	511	100
四泡流	505	505	100