Method for drill tool thread damage assessment based on APMR-GBM

Method for drill tool thread damage assessment based on APMR-GBM | 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 Method for drill tool thread damage assessment based on APMR-GBM Hao Jiang, Laibin Zhang, Jianchun Fan, Yanran Wang, Yilin Fang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8303059/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 This study addresses the prevalent issue of fatigue-induced fractures in tool joint threads during oil and gas drilling operations. In response to the practical limitations of existing metal magnetic memory (MMM) inspection methods—which are often susceptible to interference from material magnetization intensity, lift-off distance, and other influencing factors—a novel damage assessment approach based on the area-based peak-to-average ratio of magnetic signals is proposed. This method effectively mitigates the impact of such interferences on inspection outcomes. Through a combination of simulation analysis and field experiments, magnetic signal data on the thread surface were systematically collected, and multiple characteristic parameters were extracted. Machine learning techniques were further introduced to compare various classification models. Results indicate that the Gradient Boosting Machine demonstrated superior performance in identifying thread damage, with evaluation metrics such as accuracy and recall rates significantly outperforming other models. This research provides a new direction for automated and efficient damage inspection of drilling tool threads, offering practical value for enhancing the safety of drilling operations. drilling tool joint Nondestructive Testing (NDT) Metal magnetic memory (MMM) Gradient Boosting Machine(GBM) 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-8303059","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576468808,"identity":"d834956b-a3b2-4461-b6aa-f7ceb5b2b340","order_by":0,"name":"Hao Jiang","email":"","orcid":"","institution":"CHINA UNIVERSITY OF PETROLEUM (BEIJING)","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Jiang","suffix":""},{"id":576468809,"identity":"f504bbff-2493-4e8b-98ce-797ec37d4d9c","order_by":1,"name":"Laibin Zhang","email":"","orcid":"","institution":"CHINA UNIVERSITY OF PETROLEUM 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APMR-GBM","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":"drilling tool joint, Nondestructive Testing (NDT), Metal magnetic memory (MMM), Gradient Boosting 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In response to the practical limitations of existing metal magnetic memory (MMM) inspection methods\u0026mdash;which are often susceptible to interference from material magnetization intensity, lift-off distance, and other influencing factors\u0026mdash;a novel damage assessment approach based on the area-based peak-to-average ratio of magnetic signals is proposed. This method effectively mitigates the impact of such interferences on inspection outcomes. Through a combination of simulation analysis and field experiments, magnetic signal data on the thread surface were systematically collected, and multiple characteristic parameters were extracted. Machine learning techniques were further introduced to compare various classification models. Results indicate that the Gradient Boosting Machine demonstrated superior performance in identifying thread damage, with evaluation metrics such as accuracy and recall rates significantly outperforming other models. 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