The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning

The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning | 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 The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning Zhiming HUANG, WANG Shuo, WEN Hongbing, Zixin LI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6217506/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 As an important component of transmission lines, the dynamic tension parameters of cable-type structures directly influence the safety and operation of the lines. However, conventional tension detection methods commonly suffer from issues such as insufficient measurement accuracy, poor environmental adaptability, and the inability to operate on live lines, making them unsuitable for complex working conditions. To address these issues, this paper utilizes visual image technology and Broadband Phase Motion Magnification (BPMM) to amplify the micro-vibration amplitude and enhance the vibration images of transmission line cable-type components under environmental excitation.Furthermore, this study develops a combined segmentation algorithm using the U-Net network architecture and level set loss entropy to accurately capture the centroid motion trajectory of cables, thereby precisely extracting the vibration displacement time series. Finally, spectrum analysis is applied to invert the self-vibration characteristic parameters of the components and establish a tension calculation model.Experimental verification shows that the proposed method can precisely capture the micro-vibration signals induced by environmental excitation. The tension calculation results, when compared to standard sensor data, have a deviation of no more than 8%. This method successfully establishes a non-contact, high-precision measurement system for cable-type components, providing a new technical pathway for intelligent monitoring during the construction and maintenance of transmission lines. transmission lines micro vibration tension measurement deep learning image recognition vibration frequency 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-6217506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435138755,"identity":"d9cd226d-3dd8-493e-ae40-3e41a3bc7cd5","order_by":0,"name":"Zhiming HUANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACNvmDjQ8SKiR4+OUPHwQybAhr4ZNgPmzw4YyFjOQMtmSDB2fSCGuRk2BLk5zZVmFjcIPHTPJh22EiHCbdYybN2ybBIzm7x6wi4cxhBv7ZDQS0yJwxtuY5B/SLzLGyGwkV6QwSdw4Q0MKQY3ibpwxoS0PythsJZ6wZDCQSCGoxkOZhk+AxOJBgVpDYxkyEFom0JMkZQL8Y3EgxY0hscyZCC89hUCADHdZzLFki4Uwaj8QNAlrk2xtBUVlnz8/efPDjjwobOf4ZBLRgAB4S1Y+CUTAKRsEowAYA4yREgxGpojQAAAAASUVORK5CYII=","orcid":"","institution":"Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"HUANG","suffix":""},{"id":435138756,"identity":"8788a1a1-e1a0-4761-abb0-07b5c58e1bff","order_by":1,"name":"WANG Shuo","email":"","orcid":"","institution":"Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"WANG","middleName":"","lastName":"Shuo","suffix":""},{"id":435138759,"identity":"de958196-fb5f-462a-ae03-10721affee4f","order_by":2,"name":"WEN Hongbing","email":"","orcid":"","institution":"Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"WEN","middleName":"","lastName":"Hongbing","suffix":""},{"id":435138760,"identity":"7a9306f4-be77-4f80-8ad7-aa2e40783076","order_by":3,"name":"Zixin LI","email":"","orcid":"","institution":"Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2025-03-13 07:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6217506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6217506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85378996,"identity":"66cf085e-abb4-44f4-be69-6c9260abbfed","added_by":"auto","created_at":"2025-06-25 08:54:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":783813,"visible":true,"origin":"","legend":"","description":"","filename":"TheTensionMeasurementMethodforTransmissionLineSuspensionComponentsBasedonImageRecognitionandDeepLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6217506/v1_covered_f1bf3cb6-ba5d-4a81-96c3-0dc6f44b23a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning","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":"transmission lines, micro vibration, tension measurement, deep learning, image recognition, vibration frequency","lastPublishedDoi":"10.21203/rs.3.rs-6217506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6217506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs an important component of transmission lines, the dynamic tension parameters of cable-type structures directly influence the safety and operation of the lines. However, conventional tension detection methods commonly suffer from issues such as insufficient measurement accuracy, poor environmental adaptability, and the inability to operate on live lines, making them unsuitable for complex working conditions. To address these issues, this paper utilizes visual image technology and Broadband Phase Motion Magnification (BPMM) to amplify the micro-vibration amplitude and enhance the vibration images of transmission line cable-type components under environmental excitation.Furthermore, this study develops a combined segmentation algorithm using the U-Net network architecture and level set loss entropy to accurately capture the centroid motion trajectory of cables, thereby precisely extracting the vibration displacement time series. Finally, spectrum analysis is applied to invert the self-vibration characteristic parameters of the components and establish a tension calculation model.Experimental verification shows that the proposed method can precisely capture the micro-vibration signals induced by environmental excitation. The tension calculation results, when compared to standard sensor data, have a deviation of no more than 8%. This method successfully establishes a non-contact, high-precision measurement system for cable-type components, providing a new technical pathway for intelligent monitoring during the construction and maintenance of transmission lines.\u003c/p\u003e","manuscriptTitle":"The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 07:52:40","doi":"10.21203/rs.3.rs-6217506/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":"d5b339f5-e598-4f8a-92b5-93e5bf0116da","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T08:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 07:52:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6217506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6217506","identity":"rs-6217506","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}