Relay Lifespan Prediction Based on Service Performance Degradation Parameters

Relay Lifespan Prediction Based on Service Performance Degradation Parameters | 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 Relay Lifespan Prediction Based on Service Performance Degradation Parameters Yong Li, Xiaolong Huang, Jiajun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6167506/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 paper introduces a novel relay life prediction technique that leverages degradation parameters of service performance to predict the relay lifespan. The process begins with employing the soft threshold method for wavelet denoising to refine the relay performance data, thereby eliminating noise interference. Subsequently, principal component analysis (PCA) is deployed to extract the key health factors that influence relay lifespan from the dataset. The first principal component score serves as the relay's health factor. These extracted health factors are integrated into an LSTM-based model designed for predicting relay lifespan. The predictive prowess of the Long Short-Time Memory (LSTM) model is compared with that of the Autoregressive Integrated Moving Average (ARIMA) model in the context of relay life prediction. The comparison indicates that the LSTM model's superior predicting capabilities. Moreover, this approach surpasses traditional life prediction methods in terms of scientific rigor and reliability, thereby enhancing the precision of relay life predictions and curtailing operational and maintenance expenses. Relay lifespan prediction service performance degradation parameters principal component analysis (PCA) Long Short-Time Memory (LSTM) 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-6167506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432788955,"identity":"71bfa61f-b760-4d4e-acea-54200586e1e6","order_by":0,"name":"Yong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACdsaGAx8q5JghPDZitDAzNx6cccaYJC3szYd524wZiNdicBjoMN55Buzm/IcfMHwoO8zAP7uBCC2S2wyYLWekGTDOOHeYQeLOASK0GG77w2xwg4eBmbftMIOBRAIRWhLnGDAbnD/DwPyXaC0HG4BaDuQwMDMSo0USqOVgwzGIXw72nEvnkbhBQAvf8fbHn//UGCQDQ+zhgx9l1nL8MwhoUTgAoZMNgASIzYNfPRDIN0BoOwOCSkfBKBgFo2DEAgCFGEXurtyj9gAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Police College","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Li","suffix":""},{"id":432788956,"identity":"39a1c498-683c-406c-aa84-ed9dd7f1ac20","order_by":1,"name":"Xiaolong Huang","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Huang","suffix":""},{"id":432788957,"identity":"11037504-9f82-4bb9-8f34-c4516074c210","order_by":2,"name":"Jiajun Wang","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-06 06:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6167506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6167506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80023758,"identity":"4fac0dc9-d739-4323-a70d-42f64077f3f1","added_by":"auto","created_at":"2025-04-07 05:41:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2926246,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6167506/v1_covered_bc1a9473-fef1-412f-be50-87b7786c328d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relay Lifespan Prediction Based on Service Performance Degradation Parameters","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":"Relay lifespan prediction, service performance degradation parameters, principal component analysis (PCA), Long Short-Time Memory (LSTM)","lastPublishedDoi":"10.21203/rs.3.rs-6167506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6167506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper introduces a novel relay life prediction technique that leverages degradation parameters of service performance to predict the relay lifespan. 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