Intelligent Flaw Detection in Eddy Current Inspection Data through Machine Learning Model

Intelligent Flaw Detection in Eddy Current Inspection Data through Machine Learning Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intelligent Flaw Detection in Eddy Current Inspection Data through Machine Learning Model Tikesh Kumar Sahu, Thirunavukkarasu Sannasi, Anish Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6143056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 15 You are reading this latest preprint version Abstract Eddy current (EC) testing is the most widely used method for inspecting heat exchanger tubes in industries such as petrochemicals, refineries, and nuclear power plants. Heat exchangers typically consist of hundreds to thousands of tubes, and the data from EC inspections is analysed manually. This manual process is error-prone due to operator fatigue and leads to increased downtime. Thus, there is a need for an intelligent, automated flaw detection system. Although machine learning (ML) methods for this problem exist, they are often either computationally expensive or less accurate. The paper presents a robust machine learning model for automated classification of flaw signals from eddy current inspection data of heat exchanger tubes. The proposed model employs four sliding window based ingenious features namely variance, template correlation, template dynamic time warping distance and area under the signal with Random Forest supervised machine learning model, to identify flaws. The efficacy of the model is evaluated on tube inspection data acquired in a heat exchanger by comparing its performance against expert analysis. The machine learning model exhibits an impressive accuracy of 99.94% for classification of flaw signals in addition to higher desirable metrics such as precision, recall and F1-score. This work lays a strong foundation for developing a real-time, robust and reliable flaw detection system. Eddy current testing machine learning random forest flaw classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 04 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 04 Mar, 2025 First submitted to journal 03 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6143056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423761291,"identity":"99286fe0-b847-4026-8183-c3e91b7b58ad","order_by":0,"name":"Tikesh Kumar Sahu","email":"","orcid":"","institution":"Indira Gandhi Centre for Atomic Research","correspondingAuthor":false,"prefix":"","firstName":"Tikesh","middleName":"Kumar","lastName":"Sahu","suffix":""},{"id":423761292,"identity":"015d7ca0-802e-4a04-8602-2bf5cc08e25a","order_by":1,"name":"Thirunavukkarasu Sannasi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHACxgcJCI4EERrYGJgNSNbCRowyJCA/v/lZxYNfdvLmDewXH/MwWOQR1GJwjM3sRmJfsuGcAzzFxjwMEsWEtbAxALX0HGCcwcCTJjmDQSKxgaDD2ti/FQC12BOvheEYjxlDwo8DiTMY2I9JfCBGi8GxnGKgsuTkGcw8zAYfDIhxWPPxjR9//LGzncHe/vBBQkUdEQ4DAcY2IMHMYwC0lCj1IPAHRLA/IFr9KBgFo2AUjCwAAOCMN5LpbDPOAAAAAElFTkSuQmCC","orcid":"","institution":"Indira Gandhi Centre for Atomic Research","correspondingAuthor":true,"prefix":"","firstName":"Thirunavukkarasu","middleName":"","lastName":"Sannasi","suffix":""},{"id":423761296,"identity":"8dc40958-511e-4c4d-ba18-d0b219727f3b","order_by":2,"name":"Anish Kumar","email":"","orcid":"","institution":"Indira Gandhi Centre for Atomic Research","correspondingAuthor":false,"prefix":"","firstName":"Anish","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-03-03 06:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6143056/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6143056/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01229-2","type":"published","date":"2025-07-03T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86178872,"identity":"a38fbdb3-07a3-4f35-bd0d-d7c023fd3089","added_by":"auto","created_at":"2025-07-07 16:04:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690938,"visible":true,"origin":"","legend":"","description":"","filename":"MainManuscriptIntelligentFlawDetectionIGCAR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6143056/v1_covered_dbd428c4-db25-44a7-9b4d-98d32b5067c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Flaw Detection in Eddy Current Inspection Data through Machine Learning Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Eddy current testing, machine learning, random forest, flaw classification","lastPublishedDoi":"10.21203/rs.3.rs-6143056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6143056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEddy current (EC) testing is the most widely used method for inspecting heat exchanger tubes in industries such as petrochemicals, refineries, and nuclear power plants. Heat exchangers typically consist of hundreds to thousands of tubes, and the data from EC inspections is analysed manually. This manual process is error-prone due to operator fatigue and leads to increased downtime. Thus, there is a need for an intelligent, automated flaw detection system. Although machine learning (ML) methods for this problem exist, they are often either computationally expensive or less accurate. The paper presents a robust machine learning model for automated classification of flaw signals from eddy current inspection data of heat exchanger tubes. The proposed model employs four sliding window based ingenious features namely variance, template correlation, template dynamic time warping distance and area under the signal with Random Forest supervised machine learning model, to identify flaws. The efficacy of the model is evaluated on tube inspection data acquired in a heat exchanger by comparing its performance against expert analysis. The machine learning model exhibits an impressive accuracy of 99.94% for classification of flaw signals in addition to higher desirable metrics such as precision, recall and F1-score. 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