Estimation of Leak Time Parameter Based on Fusion of Leak Behavior and Data Distribution Characteristic from Gas Flow Sensor Data

Jing Liang, Shan Liang, Hao Zhang, Li Ma
{"title":"Estimation of Leak Time Parameter Based on Fusion of Leak Behavior and Data Distribution Characteristic from Gas Flow Sensor Data","authors":"Jing Liang, Shan Liang, Hao Zhang, Li Ma","doi":"10.1109/SENSORS47087.2021.9639814","DOIUrl":null,"url":null,"abstract":"The accurately estimation of leak parameter is an essential part of pipeline leakage risk assessment and guarantee of the quality of leak samples, while today’s estimation methods leverage only coarse time estimation and rely on expert experience. This paper presents a leak time parameter estimation framework fusing leak behavior and data distribution characteristic from gas flow sensor data. Under the proposed framework, a leak behavior and a data distribution characteristic extraction modules are established for guaranteeing automatic estimation of leakage time parameters in a fine-grained time range. The estimation of leak starting time for 69 different leak events are implemented based on gas flow sensors data. The estimation accuracy of 94.2% demonstrates the effectiveness of the proposed method.","PeriodicalId":6775,"journal":{"name":"2021 IEEE Sensors","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SENSORS47087.2021.9639814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The accurately estimation of leak parameter is an essential part of pipeline leakage risk assessment and guarantee of the quality of leak samples, while today’s estimation methods leverage only coarse time estimation and rely on expert experience. This paper presents a leak time parameter estimation framework fusing leak behavior and data distribution characteristic from gas flow sensor data. Under the proposed framework, a leak behavior and a data distribution characteristic extraction modules are established for guaranteeing automatic estimation of leakage time parameters in a fine-grained time range. The estimation of leak starting time for 69 different leak events are implemented based on gas flow sensors data. The estimation accuracy of 94.2% demonstrates the effectiveness of the proposed method.
基于泄漏行为与气体流量传感器数据分布特征融合的泄漏时间参数估计
泄漏参数的准确估计是管道泄漏风险评估和泄漏样本质量保证的重要组成部分,而目前的估计方法仅利用粗糙的时间估计和依赖专家经验。本文提出了一种融合泄漏行为和气体流量传感器数据分布特征的泄漏时间参数估计框架。在该框架下,建立了泄漏行为和数据分布特征提取模块,以保证在细粒度时间范围内自动估计泄漏时间参数。基于气体流量传感器数据,实现了69种不同泄漏事件的泄漏开始时间估计。估计精度达94.2%,证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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:481959085
Book学术官方微信