Online eddy current detection for micro-defects based a multi-step joint noise reduction and defect signal location

Online eddy current detection for micro-defects based a multi-step joint noise reduction and defect signal location | 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 Online eddy current detection for micro-defects based a multi-step joint noise reduction and defect signal location Xiaowei Feng, Honghong Fu, Huaishu Hou, Jinhao Li, Shuaijun Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7713003/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aiming at the problems of large background noise interference and low detection rate of micro-defects in online eddy current testing (ECT) of stainless steel welded pipes, an optimized online eddy current detection method for micro-defects is proposed. The noise reduction method combining Empirical mode Decomposition (EMD) and wavelet threshold (WT) is adopted. First, the ECT signal is decomposed into a finite number of intrinsic mode functions (IMFs) through EMD. By combining the dual-criterion screening strategy of power spectral entropy (PSD) and correlation coefficient analysis, the high-frequency noise with low correlation is removed. Then, for the high-frequency noise patterns with high correlation, the wavelet threshold denoising algorithm is adopted for denoising. The experimental results show that, compared with the separate empirical mode decomposition and wavelet threshold denoising methods, the signal-to-noise ratio of this method is increased by 5.17dB and 3.36dB respectively, and the mean square error is reduced by 120.18 and 61.50 respectively. To improve the detection rate of micro-defects, the article firstly adopts a defect signal location method combining envelope analysis and wavelet energy analysis, and then enhances the defect part of the signal. The results show that the detection rate of micro-defect stainless steel welded pipes reaches 98.5%, and the false alarm rate of defect-free stainless steel welded pipes drops to 1.8%. This method provides an effective reference for the processing of eddy current testing signals and the detection of micro-defects in stainless steel welded pipes. Eddy current testing Empirical mode decomposition Wavelet threshold Envelope analysis Wavelet energy analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7713003","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537130631,"identity":"aa516c60-af6c-4070-b157-e95fbf85aa23","order_by":0,"name":"Xiaowei Feng","email":"","orcid":"","institution":"Shanghai Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Feng","suffix":""},{"id":537130633,"identity":"c5b492fa-0e73-492e-bf73-70ddb4e6391e","order_by":1,"name":"Honghong 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location","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Eddy current testing, Empirical mode decomposition, Wavelet threshold, Envelope analysis, Wavelet energy analysis","lastPublishedDoi":"10.21203/rs.3.rs-7713003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7713003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problems of large background noise interference and low detection rate of micro-defects in online eddy current testing (ECT) of stainless steel welded pipes, an optimized online eddy current detection method for micro-defects is proposed. The noise reduction method combining Empirical mode Decomposition (EMD) and wavelet threshold (WT) is adopted. First, the ECT signal is decomposed into a finite number of intrinsic mode functions (IMFs) through EMD. By combining the dual-criterion screening strategy of power spectral entropy (PSD) and correlation coefficient analysis, the high-frequency noise with low correlation is removed. Then, for the high-frequency noise patterns with high correlation, the wavelet threshold denoising algorithm is adopted for denoising. The experimental results show that, compared with the separate empirical mode decomposition and wavelet threshold denoising methods, the signal-to-noise ratio of this method is increased by 5.17dB and 3.36dB respectively, and the mean square error is reduced by 120.18 and 61.50 respectively.\u003c/p\u003e\u003cp\u003eTo improve the detection rate of micro-defects, the article firstly adopts a defect signal location method combining envelope analysis and wavelet energy analysis, and then enhances the defect part of the signal. The results show that the detection rate of micro-defect stainless steel welded pipes reaches 98.5%, and the false alarm rate of defect-free stainless steel welded pipes drops to 1.8%. This method provides an effective reference for the processing of eddy current testing signals and the detection of micro-defects in stainless steel welded pipes.\u003c/p\u003e","manuscriptTitle":"Online eddy current detection for micro-defects based a multi-step joint noise reduction and defect signal location","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 10:35:40","doi":"10.21203/rs.3.rs-7713003/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"464231c1-b658-4df4-869b-75fc6e090d53","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T12:53:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 10:35:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7713003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7713003","identity":"rs-7713003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}