Vision to Detection: Physics-Guided Data Augmentation and Weighted Random Forests for Anomaly Detection in Electromagnetic Needle Selection

Vision to Detection: Physics-Guided Data Augmentation and Weighted Random Forests for Anomaly Detection in Electromagnetic Needle Selection | 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 Vision to Detection: Physics-Guided Data Augmentation and Weighted Random Forests for Anomaly Detection in Electromagnetic Needle Selection Laihu Peng, Song Liu, Yubao Qi, Xin Ru, Kaiyuan Shao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7899658/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The motion anomalies of needle-selection blades in electromagnetic needle selectors are subtle and rare. Limited by insufficient observation, feature extraction difficulty, and severe class imbalance due to scarce fault samples, data-driven models suffer in training and generalization. To address this, we propose a physics-guided anomaly detection method based on visually captured trajectory data. A system dynamics model generates physically consistent virtual anomaly samples to mitigate data imbalance, while normal-data pre-training enhances rare-fault recognition. Building on physics-informed data augmentation, the proposed enhanced weighted random forest achieves 97% in WAP, WAR, and WAF, with F1-scores of 0.84 for two rare anomaly classes and recall improved to 92% and 80%, outperforming mainstream data augmentation and classification methods, enabling efficient rare-fault detection under small-sample conditions. Machine Vision Class-imbalance Data Augmentation Physics-Informed Machine Learning Enhanced Random Forest Fault Diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 19 Oct, 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. 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. 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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-7899658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533949967,"identity":"f3ea2396-6f65-4a71-a716-12aad605fb65","order_by":0,"name":"Laihu Peng","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Laihu","middleName":"","lastName":"Peng","suffix":""},{"id":533949969,"identity":"96808df9-5c4a-42d8-be37-79f410e8616c","order_by":1,"name":"Song Liu","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Liu","suffix":""},{"id":533949971,"identity":"b4007ff6-b2f4-401b-9c4e-f1db23157e78","order_by":2,"name":"Yubao Qi","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Yubao","middleName":"","lastName":"Qi","suffix":""},{"id":533949972,"identity":"4c8423d5-4a4d-40b3-b18c-2195ca5c460b","order_by":3,"name":"Xin Ru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYJCCAwwVUBYP8VrOQFUTrYWBsY0ULeYzcgwPF86rs7eXSGB88LaNQd6ckBaZG2kJh2duY0vskUhgNpzbxmC4s4GAFgmJ5AOHebfxJPBIJLBJ87YxJBgcIKglseEw7xwJe6AW9t9EagHZ0mDACHQYGzNxWnieJRzmOZaQ2HPmYbPknHMShhsIamHPMf7MU1Nnz96efPDDmzIbeYK2IAHGBpARxKsfBaNgFIyCUYAbAADI1jdArB3TrQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Ru","suffix":""},{"id":533949973,"identity":"ee247a4c-72ca-44ac-9217-62d1b4f48c52","order_by":4,"name":"Kaiyuan Shao","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyuan","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2025-10-19 15:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7899658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7899658/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100595410,"identity":"2f781727-2f02-439f-967f-60aa17b96011","added_by":"auto","created_at":"2026-01-19 13:48:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":917653,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7899658/v1_covered_dfa3f17d-2745-46c1-ac33-4e8d2c5d43a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vision to Detection: Physics-Guided Data Augmentation and Weighted Random Forests for Anomaly Detection in Electromagnetic Needle Selection","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Machine Vision, Class-imbalance, Data Augmentation, Physics-Informed Machine Learning, Enhanced Random Forest, Fault Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7899658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7899658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe motion anomalies of needle-selection blades in electromagnetic needle selectors are subtle and rare. 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