• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于漏磁(MFL)测量评估钢筋缺陷的大小。

Evaluation of the Size of a Defect in Reinforcing Steel Using Magnetic Flux Leakage (MFL) Measurements.

机构信息

Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49304, Republic of Korea.

National Core Research Center for Disaster-Free and Safe Ocean Cities Construction, Dong-A University, Busan 49304, Republic of Korea.

出版信息

Sensors (Basel). 2023 Jun 6;23(12):5374. doi: 10.3390/s23125374.

DOI:10.3390/s23125374
PMID:37420540
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10303955/
Abstract

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B, B) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (B, B), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (B) bandwidth was found to increase with increasing defect width and the y-component (B) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (B). The defect areas showed a higher regression coefficient (R = 0.9079) for the x-component (B) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.

摘要

本研究旨在评估具有几种缺陷情况的 D19 尺寸增强钢筋的二维磁通量泄漏(MFL)信号(B,B)。使用经济设计的测试装置从有缺陷和新的样本中采集磁通量泄漏数据,该测试装置结合了永磁体。使用 COMSOL Multiphysics 对二维有限元模型进行数值模拟,以验证实验测试。基于 MFL 信号(B,B),本研究还旨在提高分析缺陷特征(如宽度、深度和面积)的能力。数值和实验结果均显示出高度的相关性,中位数系数为 0.920,平均值系数为 0.860。使用信号信息评估缺陷宽度时,发现 x 分量(B)带宽随缺陷宽度的增加而增加,y 分量(B)幅度随深度的增加而增加。在这项二维 MFL 信号研究中,二维缺陷的两个参数(宽度和深度)相互影响,无法单独评估。通过磁通泄漏信号的信号幅度整体变化来估算缺陷面积。x 分量(B)幅度的 3 轴传感器信号的缺陷面积显示出更高的回归系数(R = 0.9079)。确定缺陷特征与传感器信号呈正相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6e4bad746512/sensors-23-05374-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/866b20708f27/sensors-23-05374-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/dc228fcac201/sensors-23-05374-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/d00a791da5a7/sensors-23-05374-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6c553dd6a8ce/sensors-23-05374-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/cb61ccfdff41/sensors-23-05374-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/f82fd334c3a8/sensors-23-05374-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/5318e849f54b/sensors-23-05374-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6e21659456c6/sensors-23-05374-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/37457aee782e/sensors-23-05374-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/5a1ae1aeb5e1/sensors-23-05374-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/965ec076201b/sensors-23-05374-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/97a68686aef2/sensors-23-05374-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/0dccee53a2de/sensors-23-05374-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/01e2a02ccc39/sensors-23-05374-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/218b9a6e56a8/sensors-23-05374-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/e237603e5729/sensors-23-05374-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/15be99c56209/sensors-23-05374-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/cbe1e9efe098/sensors-23-05374-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/eea388951368/sensors-23-05374-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/a00c0b9f2cee/sensors-23-05374-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/c5529d8b5eec/sensors-23-05374-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6e4bad746512/sensors-23-05374-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/866b20708f27/sensors-23-05374-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/dc228fcac201/sensors-23-05374-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/d00a791da5a7/sensors-23-05374-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6c553dd6a8ce/sensors-23-05374-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/cb61ccfdff41/sensors-23-05374-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/f82fd334c3a8/sensors-23-05374-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/5318e849f54b/sensors-23-05374-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6e21659456c6/sensors-23-05374-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/37457aee782e/sensors-23-05374-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/5a1ae1aeb5e1/sensors-23-05374-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/965ec076201b/sensors-23-05374-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/97a68686aef2/sensors-23-05374-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/0dccee53a2de/sensors-23-05374-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/01e2a02ccc39/sensors-23-05374-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/218b9a6e56a8/sensors-23-05374-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/e237603e5729/sensors-23-05374-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/15be99c56209/sensors-23-05374-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/cbe1e9efe098/sensors-23-05374-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/eea388951368/sensors-23-05374-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/a00c0b9f2cee/sensors-23-05374-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/c5529d8b5eec/sensors-23-05374-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9d/10303955/6e4bad746512/sensors-23-05374-g022.jpg

相似文献

1
Evaluation of the Size of a Defect in Reinforcing Steel Using Magnetic Flux Leakage (MFL) Measurements.基于漏磁(MFL)测量评估钢筋缺陷的大小。
Sensors (Basel). 2023 Jun 6;23(12):5374. doi: 10.3390/s23125374.
2
Defect Width Assessment Based on the Near-Field Magnetic Flux Leakage Method.基于近场漏磁法的缺陷宽度评估
Sensors (Basel). 2021 Aug 11;21(16):5424. doi: 10.3390/s21165424.
3
Study on the Impact of Pole Spacing on Magnetic Flux Leakage Detection under Oversaturated Magnetization.过饱和磁化下极间距对漏磁检测影响的研究
Sensors (Basel). 2024 Aug 11;24(16):5195. doi: 10.3390/s24165195.
4
Research on the Analytical Model of Improved Magnetic Flux Leakage Signal for the Local Stress Concentration Zone of Pipelines.管道局部应力集中区改进漏磁信号的解析模型研究。
Sensors (Basel). 2022 Feb 2;22(3):1128. doi: 10.3390/s22031128.
5
Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.基于磁通泄漏传感和人工神经网络模式识别的钢丝绳无损评估自动损伤检测与量化
Sensors (Basel). 2018 Jan 2;18(1):109. doi: 10.3390/s18010109.
6
Quantitative Study on MFL Signal of Pipeline Composite Defect Based on Improved Magnetic Charge Model.基于改进磁荷模型的管道复合缺陷漏磁场信号定量研究
Sensors (Basel). 2021 May 13;21(10):3412. doi: 10.3390/s21103412.
7
Design and Optimization of an MFL Coil Sensor Apparatus Based on Numerical Survey.基于数值测量的多频线圈传感器装置的设计与优化。
Sensors (Basel). 2019 Nov 8;19(22):4869. doi: 10.3390/s19224869.
8
Phase-Extraction-Based MFL Testing for Subsurface Defect in Ferromagnetic Steel Plate.基于相位提取的铁磁钢板表面下缺陷磁通量泄漏检测
Sensors (Basel). 2022 Apr 26;22(9):3322. doi: 10.3390/s22093322.
9
A Sensor for Broken Wire Detection of Steel Wire Ropes Based on the Magnetic Concentrating Principle.一种基于磁聚焦原理的钢丝绳断丝检测传感器。
Sensors (Basel). 2019 Aug 30;19(17):3763. doi: 10.3390/s19173763.
10
Magnetic flux leakage defect size estimation method based on physics-informed neural network.基于物理信息神经网络的漏磁缺陷尺寸估计方法
Philos Trans A Math Phys Eng Sci. 2024 Jan 8;382(2264):20220387. doi: 10.1098/rsta.2022.0387. Epub 2023 Nov 20.

引用本文的文献

1
Study on the Impact of Pole Spacing on Magnetic Flux Leakage Detection under Oversaturated Magnetization.过饱和磁化下极间距对漏磁检测影响的研究
Sensors (Basel). 2024 Aug 11;24(16):5195. doi: 10.3390/s24165195.

本文引用的文献

1
Medical applications of infrared thermography: A review.红外热成像技术的医学应用:综述
Infrared Phys Technol. 2012 Jul;55(4):221-235. doi: 10.1016/j.infrared.2012.03.007. Epub 2012 Apr 13.
2
Non-Destructive Testing of Steel Corrosion Fluctuation Parameters Based on Spontaneous Magnetic Flux Leakage and Its Relationship with Steel Bar Diameter.基于自发漏磁场的钢筋锈蚀波动参数无损检测及其与钢筋直径的关系
Materials (Basel). 2019 Dec 9;12(24):4116. doi: 10.3390/ma12244116.
3
Quantitative Study on Corrosion of Steel Strands Based on Self-Magnetic Flux Leakage.
基于自磁漏的钢绞线腐蚀定量研究。
Sensors (Basel). 2018 May 2;18(5):1396. doi: 10.3390/s18051396.
4
The Non-Destructive Test of Steel Corrosion in Reinforced Concrete Bridges Using a Micro-Magnetic Sensor.基于微磁传感器的钢筋混凝土桥梁中钢材腐蚀的无损检测
Sensors (Basel). 2016 Sep 6;16(9):1439. doi: 10.3390/s16091439.
5
Theory and Application of Magnetic Flux Leakage Pipeline Detection.漏磁管道检测的理论与应用
Sensors (Basel). 2015 Dec 10;15(12):31036-55. doi: 10.3390/s151229845.
6
Non-Destructive Evaluation for Corrosion Monitoring in Concrete: A Review and Capability of Acoustic Emission Technique.混凝土中腐蚀监测的无损评估:声发射技术综述与能力
Sensors (Basel). 2015 Aug 5;15(8):19069-101. doi: 10.3390/s150819069.
7
Monitoring corrosion of steel bars in reinforced concrete structures.监测钢筋混凝土结构中钢筋的腐蚀情况。
ScientificWorldJournal. 2014 Jan 16;2014:957904. doi: 10.1155/2014/957904. eCollection 2014.