结合卷积神经网络的光载波微波干涉测量两相流量

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yan Wu;Ting Xue;Songlin Li;Zhuping Li;Bin Wu
{"title":"结合卷积神经网络的光载波微波干涉测量两相流量","authors":"Yan Wu;Ting Xue;Songlin Li;Zhuping Li;Bin Wu","doi":"10.1109/TIM.2025.3606051","DOIUrl":null,"url":null,"abstract":"The precise measurement of gas–liquid two-phase flow rate is crucial for ensuring the safety and efficiency of industrial processes. However, achieving accurate measurement remains a significant challenge. A novel method for measuring flow rates of horizontal gas–liquid two-phase flow employing optical carrier-based microwave interferometry (OCMI) technology and convolutional neural network (CNN) architecture is presented in this article, marking the first application of OCMI in gas–liquid flow rate measurement. Leveraging the distributed measurement capabilities of OCMI, the method captures the distributed information of fluid behavior along the optical fiber and gathers more comprehensive data through the combination of global and distributed interference spectra. The input data are processed utilizing dimensionality reduction techniques, including Pearson correlation and principal component analysis (PCA), and small sample sizes are expanded through data augmentation to improve the accuracy and generalization ability of the model. A decomposed CNN architecture is constructed, with convolutions performed separately along the sequence and feature dimensions, effectively overcoming the limitations of traditional demodulation methods in information extraction. The experimental results demonstrate that the proposed method accurately measures gas and liquid flow rates, offering significant advantages over other variants.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-Phase Flow Rate Measurement Utilizing Optical Carrier-Based Microwave Interferometry Integrated With Convolutional Neural Network\",\"authors\":\"Yan Wu;Ting Xue;Songlin Li;Zhuping Li;Bin Wu\",\"doi\":\"10.1109/TIM.2025.3606051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The precise measurement of gas–liquid two-phase flow rate is crucial for ensuring the safety and efficiency of industrial processes. However, achieving accurate measurement remains a significant challenge. A novel method for measuring flow rates of horizontal gas–liquid two-phase flow employing optical carrier-based microwave interferometry (OCMI) technology and convolutional neural network (CNN) architecture is presented in this article, marking the first application of OCMI in gas–liquid flow rate measurement. Leveraging the distributed measurement capabilities of OCMI, the method captures the distributed information of fluid behavior along the optical fiber and gathers more comprehensive data through the combination of global and distributed interference spectra. The input data are processed utilizing dimensionality reduction techniques, including Pearson correlation and principal component analysis (PCA), and small sample sizes are expanded through data augmentation to improve the accuracy and generalization ability of the model. A decomposed CNN architecture is constructed, with convolutions performed separately along the sequence and feature dimensions, effectively overcoming the limitations of traditional demodulation methods in information extraction. The experimental results demonstrate that the proposed method accurately measures gas and liquid flow rates, offering significant advantages over other variants.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-8\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11151564/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151564/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

气液两相流量的精确测量对于保证工业过程的安全和效率至关重要。然而,实现准确的测量仍然是一个重大挑战。本文提出了一种基于光学载流子微波干涉测量(OCMI)技术和卷积神经网络(CNN)结构的水平气液两相流流量测量新方法,这是OCMI技术在气液流量测量中的首次应用。该方法利用OCMI的分布式测量能力,捕获沿光纤分布的流体行为信息,并结合全局和分布式干涉谱收集更全面的数据。利用Pearson相关和主成分分析(PCA)等降维技术对输入数据进行处理,并通过数据扩增扩大小样本规模,提高模型的准确性和泛化能力。构造了一种分解的CNN结构,沿序列维和特征维分别进行卷积,有效克服了传统解调方法在信息提取方面的局限性。实验结果表明,该方法能够准确测量气体和液体的流量,与其他方法相比具有明显的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-Phase Flow Rate Measurement Utilizing Optical Carrier-Based Microwave Interferometry Integrated With Convolutional Neural Network
The precise measurement of gas–liquid two-phase flow rate is crucial for ensuring the safety and efficiency of industrial processes. However, achieving accurate measurement remains a significant challenge. A novel method for measuring flow rates of horizontal gas–liquid two-phase flow employing optical carrier-based microwave interferometry (OCMI) technology and convolutional neural network (CNN) architecture is presented in this article, marking the first application of OCMI in gas–liquid flow rate measurement. Leveraging the distributed measurement capabilities of OCMI, the method captures the distributed information of fluid behavior along the optical fiber and gathers more comprehensive data through the combination of global and distributed interference spectra. The input data are processed utilizing dimensionality reduction techniques, including Pearson correlation and principal component analysis (PCA), and small sample sizes are expanded through data augmentation to improve the accuracy and generalization ability of the model. A decomposed CNN architecture is constructed, with convolutions performed separately along the sequence and feature dimensions, effectively overcoming the limitations of traditional demodulation methods in information extraction. The experimental results demonstrate that the proposed method accurately measures gas and liquid flow rates, offering significant advantages over other variants.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信