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《Chinese Journal of Scientific Instrument》 2006-07
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Identification method of gas-liquid two-phase flow regime based on multi-scale information entropy feature and RBF neural network

Sun Bin Wang Qiang Zhou Yunlong(Northeast Dianli University,Jilin 132012,China)  
Based on the characteristic that the wavelet packet transform can decompose signal to different frequency bands in any time frequency resolution,a flow regime identification method based on wavelet packet multiscale information entropy is proposed.The collected pressure-difference fluctuation signals are decomposed into four-layer wavelet packets,and the decomposed signals in various frequency bands are obtained within the pass frequency band.Then the multi-scale information entropy eigenvectors of flow regime are established.The RBF neural network is trained using those eigenvectors as flow regime samples and the flow regime intelligent identification is realized.The test result shows after successful training the RBF neural network can identify four flow regimes of gas-water two-phase flow in horizontal pipe.This method introduces a new direction for flow regime identification.
【Fund】: 吉林省科技发展基金(20040513)资助项目
【CateGory Index】: O359.1
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