Article(id=1156908302073422724, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2402160, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1711382400000, receivedDateStr=2024-03-26, revisedDate=1728403200000, revisedDateStr=2024-10-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1753758033530, onlineDateStr=2025-07-29, pubDate=1736265600000, pubDateStr=2025-01-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753758033530, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753758033530, creator=13701087609, updateTime=1753758033530, updator=13701087609, issue=Issue{id=1156908295593223005, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='1', pageStart='1', pageEnd='438', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753758031985, creator=13701087609, updateTime=1765425680602, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1205845960933049001, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1205845960933049002, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=194, endPage=200, ext={EN=ArticleExt(id=1156908303327519625, articleId=1156908302073422724, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Fast Method for Calculating the Magnetic Charge Distribution of 3D Irregular Defects, columnId=1156262733675876713, journalTitle=Science Technology and Engineering, columnName=Papers·Electrical Technology, runingTitle=null, highlight=null, articleAbstract=

The magnetic flux leakage detection technology has been widely used in the field of ferromagnetic material defect detection, in which the magnetic dipole method is currently the most widely used mathematical method for predicting magnetic flux leakage from structural defects. The magnetic dipole method is primarily employed for predicting the magnetic flux leakage signal of regular defects. The unit magnetic dipole band superposition model can be used to predict the magnetic flux leakage signal of complex defects. The magnetic charge density of complex defects needs to be calculated when the model is used for prediction. However, the magnetic charge density distribution of complex defects is inhomogeneous, and the calculation is complex. Therefore, a calculation method of discrete magnetic charge density field was proposed for calculating the magnetic charge density of three-dimensional irregular defects. The computational complexity of the model was reduced by using this method and the magnetic flux leakage signal of three-dimensional irregular defects can be quickly and exactly obtained. Comparison between the signal predicted by the unit magnetic dipole band superposition model based on discrete magnetic charge density field and the signal simulated by COMSOL software demonstrates the feasibility of the method. Experimental results show that the prediction performance of the model has been significantly improved by using this method, the maximum prediction error is reduced by 90.08%, and the calculation time is reduced by 97.43%, thus a fast and effective solution for the calculation of the magnetic charge distribution of three-dimensional irregular defects is provided.

, correspAuthors=Wen-hua HAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Li-shuang RU, Wen-hua HAN), CN=ArticleExt(id=1156908418415027139, articleId=1156908302073422724, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=快速计算三维不规则缺陷磁荷分布的方法, columnId=1156262734506353627, journalTitle=科学技术与工程, columnName=论文·电工技术, runingTitle=null, highlight=null, articleAbstract=

漏磁检测技术在铁磁性材料缺陷检测领域中得到了广泛应用,其中磁偶极子法是目前应用最广泛的从结构缺陷中预测漏磁的数学方法。磁偶极子法多用于规则缺陷的漏磁信号预测,对于复杂缺陷,已有的单元磁偶极带叠加模型可以预测其漏磁信号,但是由于模型计算所需的复杂缺陷磁荷密度未知,且其分布不均匀,计算复杂。因此,提出了离散化磁荷密度场的计算方法,该方法可以快速计算三维不规则缺陷的磁荷密度,结合单元磁偶极带叠加模型直接进行漏磁信号预测,以降低计算复杂度,迅速而准确地获取三维不规则缺陷的漏磁信号。将该方法用于单元磁偶极带叠加模型得到的预测信号与COMSOL软件仿真信号比较,表明该方法是可行的。验证实验结果表明,使用该方法的单元磁偶极带叠加模型相比于原模型预测性能明显提升,预测误差最大减少90.08%,计算时间减少97.43%,为三维不规则缺陷的磁荷分布计算提供了快速有效的解决办法。

, correspAuthors=韩文花, authorNote=null, correspAuthorsNote=
* 韩文花(1976—),女,汉族,山东日照人,博士,副教授。研究方向:无损检测,智能优化算法。E-mail:
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茹黎爽(1999—),女,汉族,河南三门峡人,硕士研究生。研究方向:无损检测,优化算法。E-mail:

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茹黎爽(1999—),女,汉族,河南三门峡人,硕士研究生。研究方向:无损检测,优化算法。E-mail:

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茹黎爽(1999—),女,汉族,河南三门峡人,硕士研究生。研究方向:无损检测,优化算法。E-mail:

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Journal of Nondestructive Evaluation, 2019, 38(1): 28., articleTitle=Reduction of the velocity impact on the magnetic flux leakage signal, refAbstract=null)], funds=[Fund(id=1205908998528762142, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, awardId=51906133, language=CN, fundingSource=国家自然科学基金(51906133), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1205908993696923847, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, xref=null, ext=[AuthorCompanyExt(id=1205908993717895368, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, companyId=1205908993696923847, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China), AuthorCompanyExt(id=1205908993722089673, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, companyId=1205908993696923847, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=上海电力大学自动化工程学院, 上海 200090)])], figs=[ArticleFig(id=1205908995148153070, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=EN, label=Fig.1, caption=Defect segmentation, figureFileSmall=ej2ECvSfYac/sjH9iXNrPQ==, figureFileBig=FzU6CKE3XJdt7MoGvndYGg==, tableContent=null), ArticleFig(id=1205908995248816368, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=CN, label=图1, caption=缺陷分割, figureFileSmall=ej2ECvSfYac/sjH9iXNrPQ==, figureFileBig=FzU6CKE3XJdt7MoGvndYGg==, tableContent=null), ArticleFig(id=1205908995387228402, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=EN, label=Fig.2, caption=3D irregular defect contour, 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figureFileSmall=Y+keXg3h6BaH/6mxO6C9vg==, figureFileBig=m/sBaAzVa77YQGSDyRZQDw==, tableContent=null), ArticleFig(id=1205908998008668436, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=EN, label=Table 1, caption=

Comparison of parameters for predicted signals

, figureFileSmall=null, figureFileBig=null, tableContent=
磁荷密度的计算方法 RMSE 耗时/s
离散化磁荷密度场 0.001 7 2.31
布谷鸟算法 0.013 3 96.70
COMSOL仿真 20.70
), ArticleFig(id=1205908998071582997, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=CN, label=表1, caption=

预测信号的参数对比

, figureFileSmall=null, figureFileBig=null, tableContent=
磁荷密度的计算方法 RMSE 耗时/s
离散化磁荷密度场 0.001 7 2.31
布谷鸟算法 0.013 3 96.70
COMSOL仿真 20.70
), ArticleFig(id=1205908998151274774, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=EN, label=Table 2, caption=

Comparison of prediction accuracy for defects of different shapes

, figureFileSmall=null, figureFileBig=null, tableContent=
缺陷
状况
RMSE 时间/s
本文方法 传统方法 本文方法 传统方法
不同宽
度缺陷
6 mm 0.002 9 0.034 4 2.793 1 79.026 4
10 mm 0.002 3 0.007 2 2.179 4 87.192 8
14 mm 0.003 8 0.008 6 2.165 4 92.275 1
不同深
度缺陷
4 mm 0.001 2 0.029 1 2.252 9 89.110 3
6 mm 0.002 0 0.039 0 2.178 9 90.881 2
8 mm 0.001 8 0.027 6 2.068 4 81.613 2
不同提离
值缺陷
1 mm 0.006 5 0.012 1 2.224 1 93.524 0
2 mm 0.004 1 0.009 2 2.117 8 95.419 7
3 mm 0.004 7 0.017 4 2.082 7 85.923 2
), ArticleFig(id=1205908998222577943, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=CN, label=表2, caption=

不同形状缺陷的预测精度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
缺陷
状况
RMSE 时间/s
本文方法 传统方法 本文方法 传统方法
不同宽
度缺陷
6 mm 0.002 9 0.034 4 2.793 1 79.026 4
10 mm 0.002 3 0.007 2 2.179 4 87.192 8
14 mm 0.003 8 0.008 6 2.165 4 92.275 1
不同深
度缺陷
4 mm 0.001 2 0.029 1 2.252 9 89.110 3
6 mm 0.002 0 0.039 0 2.178 9 90.881 2
8 mm 0.001 8 0.027 6 2.068 4 81.613 2
不同提离
值缺陷
1 mm 0.006 5 0.012 1 2.224 1 93.524 0
2 mm 0.004 1 0.009 2 2.117 8 95.419 7
3 mm 0.004 7 0.017 4 2.082 7 85.923 2
), ArticleFig(id=1205908998298075417, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156908302073422724, language=EN, label=Table 3, caption=

Prediction performance of different models

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裂缝深
度/mm
RMSE 时间/s
COMSOL 本文
方法
传统
方法
COMSOL 本文
方法
传统
方法
2 0.003 1 0.003 4 0.018 3 30 2.157 8 101.970 3
4 0.004 4 0.003 8 0.041 3 32 2.460 1 81.022 6
6 0.005 9 0.005 3 0.034 8 23 2.097 5 83.847 5
8 0.009 7 0.007 6 0.057 2 24 2.090 3 81.386 3
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不同模型的预测性能

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裂缝深
度/mm
RMSE 时间/s
COMSOL 本文
方法
传统
方法
COMSOL 本文
方法
传统
方法
2 0.003 1 0.003 4 0.018 3 30 2.157 8 101.970 3
4 0.004 4 0.003 8 0.041 3 32 2.460 1 81.022 6
6 0.005 9 0.005 3 0.034 8 23 2.097 5 83.847 5
8 0.009 7 0.007 6 0.057 2 24 2.090 3 81.386 3
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快速计算三维不规则缺陷磁荷分布的方法
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茹黎爽 , 韩文花 *
科学技术与工程 | 论文·电工技术 2025,25(1): 194-200
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科学技术与工程 | 论文·电工技术 2025, 25(1): 194-200
快速计算三维不规则缺陷磁荷分布的方法
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茹黎爽 , 韩文花*
作者信息
  • 上海电力大学自动化工程学院, 上海 200090
  • 茹黎爽(1999—),女,汉族,河南三门峡人,硕士研究生。研究方向:无损检测,优化算法。E-mail:

通讯作者:

* 韩文花(1976—),女,汉族,山东日照人,博士,副教授。研究方向:无损检测,智能优化算法。E-mail:
Fast Method for Calculating the Magnetic Charge Distribution of 3D Irregular Defects
Li-shuang RU , Wen-hua HAN*
Affiliations
  • College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
出版时间: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2402160
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漏磁检测技术在铁磁性材料缺陷检测领域中得到了广泛应用,其中磁偶极子法是目前应用最广泛的从结构缺陷中预测漏磁的数学方法。磁偶极子法多用于规则缺陷的漏磁信号预测,对于复杂缺陷,已有的单元磁偶极带叠加模型可以预测其漏磁信号,但是由于模型计算所需的复杂缺陷磁荷密度未知,且其分布不均匀,计算复杂。因此,提出了离散化磁荷密度场的计算方法,该方法可以快速计算三维不规则缺陷的磁荷密度,结合单元磁偶极带叠加模型直接进行漏磁信号预测,以降低计算复杂度,迅速而准确地获取三维不规则缺陷的漏磁信号。将该方法用于单元磁偶极带叠加模型得到的预测信号与COMSOL软件仿真信号比较,表明该方法是可行的。验证实验结果表明,使用该方法的单元磁偶极带叠加模型相比于原模型预测性能明显提升,预测误差最大减少90.08%,计算时间减少97.43%,为三维不规则缺陷的磁荷分布计算提供了快速有效的解决办法。

漏磁检测  /  单元磁偶极带叠加模型  /  离散化磁荷密度场  /  三维不规则缺陷

The magnetic flux leakage detection technology has been widely used in the field of ferromagnetic material defect detection, in which the magnetic dipole method is currently the most widely used mathematical method for predicting magnetic flux leakage from structural defects. The magnetic dipole method is primarily employed for predicting the magnetic flux leakage signal of regular defects. The unit magnetic dipole band superposition model can be used to predict the magnetic flux leakage signal of complex defects. The magnetic charge density of complex defects needs to be calculated when the model is used for prediction. However, the magnetic charge density distribution of complex defects is inhomogeneous, and the calculation is complex. Therefore, a calculation method of discrete magnetic charge density field was proposed for calculating the magnetic charge density of three-dimensional irregular defects. The computational complexity of the model was reduced by using this method and the magnetic flux leakage signal of three-dimensional irregular defects can be quickly and exactly obtained. Comparison between the signal predicted by the unit magnetic dipole band superposition model based on discrete magnetic charge density field and the signal simulated by COMSOL software demonstrates the feasibility of the method. Experimental results show that the prediction performance of the model has been significantly improved by using this method, the maximum prediction error is reduced by 90.08%, and the calculation time is reduced by 97.43%, thus a fast and effective solution for the calculation of the magnetic charge distribution of three-dimensional irregular defects is provided.

magnetic flux leakage testing  /  magnetic dipole band superposition model  /  magnetic charge density  /  3D irregular defects
茹黎爽, 韩文花. 快速计算三维不规则缺陷磁荷分布的方法. 科学技术与工程, 2025 , 25 (1) : 194 -200 . DOI: 10.12404/j.issn.1671-1815.2402160
Li-shuang RU, Wen-hua HAN. Fast Method for Calculating the Magnetic Charge Distribution of 3D Irregular Defects[J]. Science Technology and Engineering, 2025 , 25 (1) : 194 -200 . DOI: 10.12404/j.issn.1671-1815.2402160
铁磁性金属材料因其优异的力学性能而广泛应用于工程设备或结构中,如输气管道[1]。为了保持其完整性和降低失败的风险,有必要定期或根据需要进行检查。漏磁检测是一种在铁磁性材料检测领域广泛应用的关键无损检测技术[2-3]。其基本原理是对铁磁性材料进行磁化,当表面或近表面存在缺陷时,磁场会发生畸变,形成与缺陷几何特征相关的漏磁场。通过捕获漏磁场信号,实现对缺陷的精准检测[4-5]。漏磁检测主要分为漏磁场信号提取与处理和缺陷反演两个步骤[6],建立准确快速的前向模型,即漏磁检测的正演问题,是漏磁检测的基础。
目前,漏磁检测中正演问题的计算模型主要有3种:启发式模型[7-10]、数值计算模型[11-14]和解析模型[15-20]。启发式模型一般采用神经网络模型[7],即建立漏磁信号特征值的数据库,大多使用机器学习[8]或者深度学习[9]对漏磁信号的特征值进行处理,其精确度取决于训练数据的数量和质量[10],通常更适用于规则形状的缺陷。数值计算模型中有限元模型是应用最广泛的方法,例如锅形励磁结构的漏磁检测装置[11]和二维缺陷的有限元模型[12]等,它的优点在于计算精度高,能实现复杂缺陷漏磁信号的精确模拟[13-14],其缺点是计算量较大,成本高。解析模型主要是磁偶极子模型[15],磁偶极子解析模型早期只用于规则缺陷的信号计算[16],在轴向方向施加磁场的情况下,由于整个缺陷深度一致,槽状缺陷的磁荷密度通常假设为恒定值,不同缺陷的磁荷密度大小由其宽度和深度决定[17]。它的优点在于模型简单、计算量小且计算时间较短,但因不规则缺陷磁荷分布的复杂性致使该方法难以用于复杂缺陷的漏磁信号计算。
实际生产生活中,大部分缺陷形状都不规则,因此为了满足需要,衍生出适应复杂缺陷漏磁检测的解析模型[18],该模型对于磁荷密度计算简单地认为在非缺陷区域磁荷近似为常数,在缺陷附近假设缺陷表面积累的磁荷密度为常数。同样用于复杂缺陷漏磁检测的模型有改进的偶极子模型[19]和单元磁偶极带叠加模型[20]等,前者指出感应磁荷密度计算公式,但是公式均为积分形式,计算过程难以实现;后者磁荷密度计算使用布谷鸟搜索算法迭代求解,整个模型的漏磁信号计算精度和速度取决于布谷鸟搜索的准确度和迭代速度。
在上述的磁荷密度计算方法中,磁荷密度计算的复杂性以及计算结果的随机性是导致模型预测误差大、计算时间长等问题的重要原因。如何快速、准确地计算出三维不规则缺陷的磁荷分布是解析模型有效应用于复杂缺陷漏磁检测的关键。
因此,现提出一种快速计算三维不规则缺陷磁荷分布的离散化磁荷密度场计算方法,通过对缺陷离散化计算缺陷的磁荷密度场,并将其代入单元磁偶极带叠加模型计算漏磁信号,验证该方法的有效性和可行性。以期利用该方法降低磁荷密度的计算时长和计算量,结合单元磁偶极带叠加模型能够快速、准确地预测三维不规则缺陷的漏磁信号。
漏磁重构的正演问题就是由已知缺陷求解漏磁场。使用单元磁偶极带叠加模型[20]计算漏磁场信号。利用深度矩阵将待求解的目标区域(region of interest, ROI)进行定义,并剖分成N×N个矩形槽单元磁偶极带,缺陷剖分情况如图1所示。同时,采样平面也相应划分为N2个单元网格。
单元磁偶极带叠加模型将N×N个单元磁偶极带在采样点处的信号作叠加处理得到ROI的漏磁检测信号,采样点的磁通密度为
Bz(ci)= j = 1 N 2 μ 0 V j 4 π z i - z j c i - s j 3δj
式(1)中:N2为单元磁偶极带的总数目;δj为单元磁偶极带sj的磁荷密度;Vj为第j个单元磁偶极带的体积;zizj分别为cisj的径向坐标; c i - s jsjci处的距离;μ0为真空磁导率。其中磁荷密度值δj是未知的,因此该模型的预测效果依赖于准确的缺陷磁荷分布数值计算方法。
原模型中利用布谷鸟搜索算法对磁荷分布矩阵进行优化求解,布谷鸟搜索算法属于群智能优化算法,它通过不断地迭代使用莱维随机飞行和偏好随机游走部件来搜索新的个体,具有逻辑简单、容易实现等特点[21],在用于解决简单的最优化问题中效果显著。
但由于铁磁性材料的非线性特性,三维不规则缺陷的磁荷密度不是一个常值,而是一个N2维的数组,使用布谷鸟搜索算法计算N2维数组时,迭代稳定性较差,导致单元磁偶极带叠加模型的漏磁信号预测效果随机性高[22]。正演过程是整个缺陷重构过程的基础,正演模型的预测精度直接影响缺陷重构准确度,因此提出了一种快速、准确的三维不规则缺陷磁荷密度计算方法,可以用于单元磁偶极带叠加模型。
不规则缺陷的磁荷密度计算如式(2)所示。
δ(xj,yj,zj)=IM(xj,yj,zj)
式(2)中:(xj,yj,zj)为磁偶极带sj中心点tj的坐标;Itj到各采样点的单位向量;M(xj,yj,zj)为磁偶极带sj上施加的磁化强度。
对于三维不规则缺陷,由于缺陷各点深度不同,所以每个磁偶极带的磁荷密度值均不相同,形成一个磁荷密度场。因此,使用单元磁偶极带叠加模型在对三维不规则缺陷进行建模时,需要对其磁荷密度场进行离散化处理,每一个单元磁偶极带对应一个磁荷密度值,提出离散化磁荷密度场的计算方法,具体计算步骤如下。
各单元磁偶极带的磁荷密度使用中心坐标代替计算。为计算方便将深度矩阵DN×N的矩阵转化为1×N2的向量d,即
D 11 D 12 D 1 D 21 D 22 D 2 N   D N 1 D N 2 D N N d 1 d 2 d k d N 2
设定磁化强度沿轴向方向,所以单位向量I的轴向分量Ix
Ix= A 11 A 12 A 1 N 2 A 21 A 22 A 2 N 2 A k m A N 2 1 A N 2 2 A N 2 N 2
式(4)中:
Akm= x '   m - x k ( x '   m - x k ) 2 + ( y '   m - y k ) 2 + ( h - d k ) 2
Mx=M [ 1 1 1 ] N 2 T
式中:(x'm,y'm,z'm)为第m个采样点坐标;dk为第k个磁偶极带的深度;mk的取值范围为1~N2M为整个模型施加的磁化强度,通常设为定值。代入磁荷密度计算公式为
δ'=IxMx
考虑到缺陷附近试样表面的泄漏和平行于试样表面的缺陷表面的泄漏对磁荷密度的影响[23],在计算缺陷的磁荷密度时需要抵消去表面泄漏对计算结果的影响,即减去无缺陷时试样表面存在的磁荷密度δ0,如式(8)所示。
δ=δ'-δ0
式(8)中:
δ0=I0xMx
I0x= A '   11 A '   12 A '   1 N 2 A '   21 A '   22 A '   2 N 2 A '   k m A '   N 2 1 A '   N 2 2 A '   N 2 N 2
A'km= x '   m - x k ( x '   m - x k ) 2 + ( y '   m - y k ) 2 + ( h - 0 ) 2
式中:I0x为缺陷深度为0时每个磁偶极带到各采样点单位向量的轴向分量。
将计算得到的磁荷密度值代入单元磁偶极带叠加模型计算公式中,得到所求缺陷的漏磁场信号。
实际情况下认为COMSOL仿真出的漏磁信号是非常接近于真实信号的,一般验证前向模型时,使用COMSOL软件仿真的信号作为实测信号。为了验证该方法的有效性,使用离散化磁荷密度场的计算方法结合单元磁偶极带叠加模型,并与使用其他方法预测磁荷分布的单元磁偶极带叠加模型和COMSOL仿真的计算结果进行比较。使用的三维不规则缺陷如图2所示,缺陷的磁偶极带与采样平面的点分割均为10×10,即100个,缺陷深度为0~10 mm,检测提离值为1 mm。3种方法计算出的漏磁信号如图3所示。
图3中可以看出,相较于使用布谷鸟搜索算法计算磁荷分布的单元磁偶极带叠加模型,离散化磁荷密度场计算方法与单元磁偶极带叠加模型相结合的预测信号更接近于COMSOL软件仿真的漏磁信号。
使用本文方法的预测信号与COMSOL软件仿真漏磁信号的相对误差如图4所示,由于缺陷边缘磁场分布影响较大,所以误差较大,但采样平面中间区域误差较小,基本在10%以内。
将基于离散化磁荷密度场的单元磁偶极带叠加模型计算的预测信号和基于布谷鸟算法计算磁荷分布的单元磁偶极带叠加模型的预测信号分别与COMSOL软件仿真的漏磁信号做均方根误差(root mean square error, RMSE),如表1所示。
使用本文方法的预测信号均方根误差在2%以内,是基于布谷鸟算法的单元磁偶极带叠加模型预测信号均方根误差的12.8%,且计算速度快于使用布谷鸟算法的模型,是使用布谷鸟搜索算法模型的2.4%。说明所提离散化磁荷密度场的计算方法可用于三维不规则缺陷磁荷密度计算,且适用于单元磁偶极带叠加模型。
为验证所提的磁荷密度计算方法在单元磁偶极带叠加模型中的可行性,进一步验证基于所提方法的单元磁偶极带叠加模型在缺陷宽度、深度以及提离值变化的情况下对漏磁场变化的影响。3组缺陷设置如下:提离值为2 mm,缺陷深度为8 mm,缺陷宽度分别为6、10和14 mm的3个缺陷;提离值为2 mm,缺陷宽度为10 mm,缺陷深度分别为4、6和8 mm的3个缺陷;缺陷深度为8 mm,宽度为10 mm,提离值分别为1、2、3 mm的3个缺陷。预测这3组缺陷的漏磁信号与COMSOL仿真出的径向分量进行对比,如图5~图7所示,其中所建模型参数和计算条件与缺陷1一致。
图5中,随着缺陷宽度的增加,漏磁信号的峰值越靠近轴向方向的两侧。图6中,随着缺陷深度越深,漏磁信号的峰值越大。图7中,随着提离值的增加,漏磁信号的峰值越小。因此,从图5~图7中可以看出,使用所提磁荷密度计算方法的前向模型得到的预测信号随缺陷宽度、深度和提离值改变的变化趋势与COMSOL软件仿真信号完全一致,验证了该方法的可行性。
为了进一步验证该方法的可行性,使用均方根误差衡量信号偏差度,表2给出了分别使用所提方法和布谷鸟算法计算磁荷分布的单元磁偶极带叠加模型的预测信号与COMSOL仿真信号的均方根误差和所用时间对比数据。从表2中可以看出,单元磁偶极带叠加模型使用离散化磁荷密度场计算方法得到的预测信号相较于使用布谷鸟搜索算法计算磁荷分布的预测信号,RMSE更低,同时耗时更短。使用布谷鸟搜索算法的单元磁偶极带叠加模型的计算时间为使用本文方法的40倍左右,RMSE为使用本文方法的2~24倍。可以看出使用布谷鸟搜索算法的预测误差较大,耗时较长,且预测效果不稳定。而使用本文方法的预测信号其均方根误差值基本维持在0.001~0.007,进一步说明该方法用于单元磁偶极带叠加模型的预测精度几乎不受缺陷轮廓和提离值影响。
三维缺陷漏磁检测实质上就是通过传感器阵列获取缺陷上方一定距离漏磁信号。每个传感器都是单独工作的,可以看作是一个采样点,因此一个三维缺陷的形状可用一个N×N维的深度矩阵表示。为验证所提出的磁荷密度计算方法在实际漏磁检测应用中的适用性,采用波兰格但斯克工业大学Zbigniew Usarek教授团队公开的漏磁检测实验数据集[24]作为实际测量数据。该数据集包括通过漏磁技术测得的原始信号,信号由线性霍尔传感器A1324测得,包含了8组具有不同速度效应的漏磁检测数据,检测对象为S355钢材料试件上的4组狭长裂缝,这些裂缝深度各不相同,检测装置的结构如图8所示。
对传感器测得的数据进行预处理得到实测信号。使用所提离散化磁荷密度场的方法计算缺陷的磁荷分布,然后结合单元磁偶极带叠加模型得到裂缝缺陷的预测信号。深度为2 mm和4 mm的裂缝缺陷的预测信号与实测信号对比分别如图9图10所示。
图9图10可以看出,使用所提离散化磁荷密度场的方法、COMSOL建模和使用布谷鸟算法得到的漏磁信号与实测信号呈现一致的变化趋势,且峰值和谷值也基本匹配。为了验证所提方法在实际漏磁检测应用中的适用性,使用上述3种模型对不同深度裂缝缺陷进行漏磁信号预测,比较3种预测结果与实测信号的均方根误差以及计算时间,具体结果如表3所示。
表3中可以看出,以4 mm和8 mm裂缝为例,本文方法的预测误差与COMSOL计算误差基本相符。相比于使用布谷鸟算法的预测信号,本文方法的误差分别降低了90.8%和86.71%,同时计算时间分别减少了96.96%和97.43%。与COMSOL仿真相比,本文方法在计算时间上减少了92.31%和91.29%。这一结果表明,所提出的离散化磁荷密度场概念和计算方法在实际漏磁检测应用中是可行的,且基于该方法的单元磁偶极带叠加模型预测结果不仅提高了计算精度,而且显著减少了计算时间。
通过提出离散化磁荷密度场的计算方法,提高了单元磁偶极带叠加模型的计算精度和速度。该方法可以快速计算三维不规则缺陷的磁荷密度,结合单元磁偶极带叠加模型直接进行漏磁信号预测,以降低计算复杂度,迅速而准确地获取三维不规则缺陷的漏磁信号。将得到的预测信号与COMSOL软件仿真信号比较,验证了该方法的可行性,同时验证了该方法不受缺陷宽度、深度以及提离值数值变化的影响,进一步验证了该方法的可行性。通过实际裂缝缺陷数据验证,使用该方法的单元磁偶极带叠加模型相比于原模型预测性能明显提升,预测误差最大降低90.8%,计算时间最多减少97.43%。因此三维不规则缺陷的磁荷分布计算可以使用离散化磁荷密度场的方法快速有效地解决。
  • 国家自然科学基金(51906133)
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doi: 10.12404/j.issn.1671-1815.2402160
  • 接收时间:2024-03-26
  • 首发时间:2025-07-29
  • 出版时间:2025-01-08
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  • 收稿日期:2024-03-26
  • 修回日期:2024-10-09
基金
国家自然科学基金(51906133)
作者信息
    上海电力大学自动化工程学院, 上海 200090

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* 韩文花(1976—),女,汉族,山东日照人,博士,副教授。研究方向:无损检测,智能优化算法。E-mail:
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