Abstract：The flow pattern identification algorithm based on the sparsity of measured capacitance for electrical capacitance tomography (ECT) is proposed. Firstly, the over-complete dictionary of normalized capacitance measurement for investigated flow patterns is built, with which the sparse representation of the sample can be obtained. And then, the basic requirements of sparse reconstruction can be met. The orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse solution of each standard sample using the over-complete dictionary.Finally, the flow pattern is identified according to the linear correlation between the sample to be identified and the sparse solution of the standard sample. Simulation and static experiments are carried out for the five typical two-phase flow patterns, and the correct identification rate is higher than 98%.
［1］张立峰, 宋亚杰. 基于梯度投影稀疏重建算法的电容层析成像图像重建 ［J］. 计量学报, 2019, 40 (4): 631-635.
Zhang L F, Song Y J. Image Reconstruction for Electrical Capacitance Tomography Based on Barzilai-Borwein Gra-dient Projection for Sparse Reconstruction Algorithm ［J］. Acta Metrologica Sinica, 2019, 40 (4): 631-635.
［2］Huang S S, Sun Z Q, Zhou T. Application of Time-frequency Entropy from Wake Oscillation to Gas-liquid Flow Pattern Identification ［J］. Journal of Central South University, 2018, 25 (7): 1690-1700.
［3］彭黎辉, 张宝芬, 姚丹亚. 基于模糊神经元网络的两相流流型辩识方法 ［J］. 模式识别与人工智能, 1997, 10 (4): 332-337.
Peng L H, Zhang B F, Yao D Y. A New Flow Pattern Recognition Method Based on Fuzzy Logical Neural Net-woks ［J］. PR&AI, 1997, 10 (4): 332-337.
［4］邵晓寅, 黄志尧, 冀海峰, 等. 基于电容层析成像和模糊模式识别的油气两相流流型辨识新方法的研究 ［J］. 高校化学工程学报, 2003, 17 (6): 616-621.
Shao X Y, Huang Z Y, Ji H F, et al. Study on Flow Pattern Identification of Gas-oil Two-phase Flow Based on Electrical Capacitance Tomography and Fuzzy Pattern Re-cognition ［J］. Journal of Chemical Engineering of Chinese Universities, 2003, 17 (6): 616-621.
［5］Xie D L, Huang Z Y, Ji H F, et al. An Online Flow Pattern Identification System for Gas-Oil Two-Phase Flow Using Electrical Capacitance Tomography ［J］. IEEE Transactions on Instrumentation and Measurement, 2006, 55 (5): 1833-1838.
［6］Zhang L F, Wang H X. Identification of Oil-gas Two-phase Flow Pattern Based on SVM and Electrical Capacitance Tomography Technique ［J］. Flow Meas-urement and Instrumentation, 2010, 21 (1): 20-24.
［7］宋蕾, 陈德运, 姚玉梅, 等. Elman神经网络在ECT系统流型辨识中的应用 ［J］. 哈尔滨理工大学学报, 2014, 19 (5): 103-108.
Song L, Chen D Y, Yao Y M, et al. Application of Elman Neural Network in Pattern Identification for Elec-trical Capacitance Tomography ［J］. Journal of Harbin University of Science and Technology, 2014, 19 (5): 103-108.
［8］龙军, 冀海峰, 王保良, 等. 经验模态分解和小波分析在小通道气液两相流流型辨识中的应用 ［J］. 高校化学工程学报, 2011, 25 (5): 759-764.
Long J, Ji H F, Wang B L, et al. Application of Empirical Mode Decomposition and Wavelet Analysis to Small Channel Gas-Liquid Two-Phase Flow Pattern Identi-fication ［J］. Journal of Chemical Engineering of Chinese Universities, 2011, 25 (5): 759-764.
［9］张立峰, 蒋玉虎. 电容层析成像三维图像重建研究 ［J］. 计量学报, 2019, 40 (3): 462-465.
Zhang L F, Jiang Y H. Study of Three-dimensional Image Reconstruction for Electrical Capacitance Tomography ［J］. Acta Metrologica Sinica, 2019, 40 (3): 462-465.
［10］张立峰. 压缩感知在电容层析成像中的应用 ［J］. 北京航空航天大学学报, 2017, 43 (11) : 2316-2321.
Zhang L F. Compressed Sensing Application to Electr-ical Capacitance Tomography ［J］. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43 (11): 2316-2321.
［11］石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展 ［J］. 电子学报, 2009, 37 (5): 1070-1081.
Shi G M, Liu D H, Gao D H, et al. Advances in Theory and Application of Compressed Sensing ［J］. Acta Electronic Sinica, 2009, 37 (5): 1070-1081.
［12］吴新杰, 黄国兴, 王静文. 压缩感知在电容层析成像流型辨识中的应用 ［J］.光学精密工程, 2013, 21 (4): 1062-1068.
Wu X J, Huang G X, Wang J W. Application of Compressed Sensing to Flow Pattern Identification of ECT ［J］. Optics and Precision on Engineering, 2013, 21 (4): 1062-1068.
［13］方红, 杨海蓉. 贪婪算法与压缩感知理论 ［J］. 自动化学报, 2011, 37 (12): 1413-1421.
Fang H, Yang H R. Greedy Algorithm and Compessed Sensing ［J］. Acta Automatica Sinica, 2011, 37 (12): 1413-1421.
2021, Vol. 42 
