Calibration of a Finite Element Forward Model in Eddy Current Inspection
We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative...
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Veröffentlicht in: | IEEE sensors journal 2022-06, Vol.22 (11), p.10699-10707 |
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creator | Hampton, Joel Tesfalem, Henok Dorn, Oliver Fletcher, Adam Peyton, Anthony Brown, Matthew |
description | We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy. |
doi_str_mv | 10.1109/JSEN.2022.3167253 |
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An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2022.3167253</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Calibration ; Coils ; Conductivity ; constrained optimization ; Constraints ; Depth profiling ; Eddy current testing ; Eddy currents ; Finite element analysis ; Finite element method ; finite element model ; Inductance ; inductance spectroscopy ; inverse problem ; Iterative methods ; Mathematical models ; Neural networks ; Nondestructive testing ; Optimization ; Parameters ; Signal to noise ratio ; Three dimensional models ; Tuning</subject><ispartof>IEEE sensors journal, 2022-06, Vol.22 (11), p.10699-10707</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-e381b4dd54504b68ee5c67ad6c41b215a4613470a4ee69acd5fa8dee17335b553</citedby><cites>FETCH-LOGICAL-c336t-e381b4dd54504b68ee5c67ad6c41b215a4613470a4ee69acd5fa8dee17335b553</cites><orcidid>0000-0002-5740-348X ; 0000-0001-8748-8253 ; 0000-0002-2285-2090 ; 0000-0003-4741-238X ; 0000-0002-1529-7295</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9758755$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9758755$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hampton, Joel</creatorcontrib><creatorcontrib>Tesfalem, Henok</creatorcontrib><creatorcontrib>Dorn, Oliver</creatorcontrib><creatorcontrib>Fletcher, Adam</creatorcontrib><creatorcontrib>Peyton, Anthony</creatorcontrib><creatorcontrib>Brown, Matthew</creatorcontrib><title>Calibration of a Finite Element Forward Model in Eddy Current Inspection</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.</description><subject>Algorithms</subject><subject>Calibration</subject><subject>Coils</subject><subject>Conductivity</subject><subject>constrained optimization</subject><subject>Constraints</subject><subject>Depth profiling</subject><subject>Eddy current testing</subject><subject>Eddy currents</subject><subject>Finite element analysis</subject><subject>Finite element method</subject><subject>finite element model</subject><subject>Inductance</subject><subject>inductance spectroscopy</subject><subject>inverse problem</subject><subject>Iterative methods</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Nondestructive testing</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Signal to noise ratio</subject><subject>Three dimensional models</subject><subject>Tuning</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF9LwzAUxYMoOKcfQHwJ-NyZNH_3KKVzk6kPKvgW0uYWMrpmJh2yb7-WDZ_uhXvOPZwfQveUzCgl86fXz_J9lpM8nzEqVS7YBZpQIXRGFdeX485Ixpn6uUY3KW0IoXMl1AQtC9v6Ktrehw6HBlu88J3vAZctbKHr8SLEPxsdfgsOWuw7XDp3wMU-xvG66tIO6tF8i64a2ya4O88p-l6UX8UyW3-8rIrndVYzJvsMmKYVd05wQXglNYCopbJO1pxWORWWS8q4IpYDyLmtnWisdgBUMSYqIdgUPZ7-7mL43UPqzSbsYzdEmnwoTrTWig8qelLVMaQUoTG76Lc2HgwlZgRmRmBmBGbOwAbPw8njAeBfP2DSasg9Arw2ZhU</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Hampton, Joel</creator><creator>Tesfalem, Henok</creator><creator>Dorn, Oliver</creator><creator>Fletcher, Adam</creator><creator>Peyton, Anthony</creator><creator>Brown, Matthew</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-5740-348X</orcidid><orcidid>https://orcid.org/0000-0001-8748-8253</orcidid><orcidid>https://orcid.org/0000-0002-2285-2090</orcidid><orcidid>https://orcid.org/0000-0003-4741-238X</orcidid><orcidid>https://orcid.org/0000-0002-1529-7295</orcidid></search><sort><creationdate>20220601</creationdate><title>Calibration of a Finite Element Forward Model in Eddy Current Inspection</title><author>Hampton, Joel ; Tesfalem, Henok ; Dorn, Oliver ; Fletcher, Adam ; Peyton, Anthony ; Brown, Matthew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c336t-e381b4dd54504b68ee5c67ad6c41b215a4613470a4ee69acd5fa8dee17335b553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Coils</topic><topic>Conductivity</topic><topic>constrained optimization</topic><topic>Constraints</topic><topic>Depth profiling</topic><topic>Eddy current testing</topic><topic>Eddy currents</topic><topic>Finite element analysis</topic><topic>Finite element method</topic><topic>finite element model</topic><topic>Inductance</topic><topic>inductance spectroscopy</topic><topic>inverse problem</topic><topic>Iterative methods</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Nondestructive testing</topic><topic>Optimization</topic><topic>Parameters</topic><topic>Signal to noise ratio</topic><topic>Three dimensional models</topic><topic>Tuning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hampton, Joel</creatorcontrib><creatorcontrib>Tesfalem, Henok</creatorcontrib><creatorcontrib>Dorn, Oliver</creatorcontrib><creatorcontrib>Fletcher, Adam</creatorcontrib><creatorcontrib>Peyton, Anthony</creatorcontrib><creatorcontrib>Brown, Matthew</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hampton, Joel</au><au>Tesfalem, Henok</au><au>Dorn, Oliver</au><au>Fletcher, Adam</au><au>Peyton, Anthony</au><au>Brown, Matthew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Calibration of a Finite Element Forward Model in Eddy Current Inspection</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>22</volume><issue>11</issue><spage>10699</spage><epage>10707</epage><pages>10699-10707</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2022.3167253</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-5740-348X</orcidid><orcidid>https://orcid.org/0000-0001-8748-8253</orcidid><orcidid>https://orcid.org/0000-0002-2285-2090</orcidid><orcidid>https://orcid.org/0000-0003-4741-238X</orcidid><orcidid>https://orcid.org/0000-0002-1529-7295</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Calibration Coils Conductivity constrained optimization Constraints Depth profiling Eddy current testing Eddy currents Finite element analysis Finite element method finite element model Inductance inductance spectroscopy inverse problem Iterative methods Mathematical models Neural networks Nondestructive testing Optimization Parameters Signal to noise ratio Three dimensional models Tuning |
title | Calibration of a Finite Element Forward Model in Eddy Current Inspection |
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