Electrical Resistance Tomography and Flow Pattern Identification Method Based on Deep Residual Neural Network

Authors

Weiguo Tong, Department of Automation, North China Electric Power University, Baoding 071003, China;Follow
Shichao Zeng, Department of Automation, North China Electric Power University, Baoding 071003, China;Follow
Lifeng Zhang, Department of Automation, North China Electric Power University, Baoding 071003, China;
Zhe Hou, Department of Automation, North China Electric Power University, Baoding 071003, China;
Jiayue Guo, Department of Automation, North China Electric Power University, Baoding 071003, China;

Abstract

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.

Recommended Citation

Tong, Weiguo; Zeng, Shichao; Zhang, Lifeng; Hou, Zhe; and Guo, Jiayue (2022) "Electrical Resistance Tomography and Flow Pattern Identification Method Based on Deep Residual Neural Network," Journal of System Simulation: Vol. 34: Iss. 9, Article 12.
DOI: 10.16182/j.issn1004731x.joss.21-0294
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss9/12

First Page

2028

Revised Date

2021-05-12

DOI Link

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

Last Page

2036

CLC

TP274;TP391

Recommended Citation

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.

Corresponding Author

Shichao Zeng,zsc6052@163.com

DOI

10.16182/j.issn1004731x.joss.21-0294