DDAG支持向量机在ERT系统流型识别中的应用

摘要

针对两相流体流动特性复杂、流型识别准确率低等问题，提出一种能够提高两相流流型识别率的方法。首先采用小波包分析对ERT系统测量的压差波动信号进行特征提取；然后通过计算类间不可分离程度为每个节点选取最易分的两类构造DDAG支持向量机多类分模型；最后将特征数据输入分类模型进行流型识别。通过实验对比，四种流型识别的准确率要明显高于其它常用方法的流型识别。结果表明，小波包分析和DDAG支持向量机多类分类算法较大提高了油/水两相流流型识别的精度，是一种有效的流型识别方法。%According to the fact that two-phase fluid has complex flow characteristic, and the accuracy of flow regime is low. In this paper,a method of improving recognizing rate of flow regime is presented. Firstly,wavelet packet anal-ysis is adopted to extract the feature of the differential pressure fluctuation signal which is measured by electrical resis-tance tomography system,then the improved DDAGSVM muliticlass model is structured according to computing the in-ter-class separability which can distinguish two class easily for each mode. Finally the extracted feature data is taken as input information of the multi-class support vector machine of improved DDAG, so the four kinds of two-phase flow regime can be identified. Through experiment comparing,the accuracy rate of flow regime identification in this paper is higher than other method. Results show that the precision of two-phase flow regime identification is improved by the method of the wavelet packet analysis and DDAG support vector machine. It is an effective method of regime identification.