计量学报2023，Vol.44Issue(12)：1819-1826,8.DOI:10.3969/j.issn.1000-1158.2023.12.06
基于Choi-Williams分析与神经网络的两相流流型识别
Two-Phase Flow Pattern Identification Based on Choi-Williams Analysis and Neural Network
摘要
Abstract
A flow pattern recognition method based on Choi-Williams analysis and neural network is proposed.The array conductivity sensor is used to obtain the flow pattern information of gas-liquid two-phase flow in vertical rising pipeline,and the multivariate measurement data are denoised and dimensionally reduced.Further,Choi-Williams analysis is used to convert it into time-frequency spectrogram,and the data set is constructed.CNN,VGG-16 and ResNet-18 network models are built respectively,and the time-frequency spectrograms of different flow patterns are used as network input for training and testing.The recognition results show that Choi-Williams analysis can effectively extract the characteristics of different flow pattern signals,and the three networks have high recognition accuracy,among which ResNet-18 network has the highest accuracy,with an average flow pattern recognition rate of 99.4%.关键词
计量学/流型识别/Choi-Williams分析/神经网络/阵列电导传感器/气液两相流Key words
metrology/flow pattern identification/Choi-Williams analysis/neural network/array conductivity sensor/gas-liquid two-phase flow分类
通用工业技术引用本文复制引用
张立峰,张思佳,刘帅..基于Choi-Williams分析与神经网络的两相流流型识别[J].计量学报,2023,44(12):1819-1826,8.基金项目
国家自然科学基金(61973115) （61973115）
