{"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}
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.
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.