An automatic data augment method for remaining useful life prediction of aeroengines

An automatic data augment method for remaining useful life prediction of aeroengines | 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 An automatic data augment method for remaining useful life prediction of aeroengines Zequan Wang, Hanqing Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7162348/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 The prediction of remaining service life in complex aviation engine systems is of great significance for airlines to develop maintenance plans for engines and reduce maintenance cost.However, the complex operating conditions of the engine and insufficient fault mode data limit the prediction accuracy. One direction to solve such problems is data augmentation, which aims to generate synthetic data from real datasets to expand training samples and improve the model's generalization ability.Admittedly, There are already many mature data augmentation methods, but the optimal data augmentation strategy for RUL prediction tasks varies in different situations. Confirming which data augmentation strategy is most suitable for the current remaining useful life prediction problem requires human experience or extensive parameter experiments.This work proposes an automatic data augmentation method(AdaRUL),Build an automatic search space and use reinforcement learning algorithms to search for the optimal strategy in the automatic search space to expand the sample dataset. The experiments conducted on the C-MAPSS public dataset provided by NASA demonstrate that AdaRUL has successfully generated high fidelity multivariate monitoring data. In addition, these generated data effectively support RUL prediction tasks and significantly improve the predictive ability of underlying deep learning models. Remaining Useful Life Prediction Data Augment Reinforcement Learning Deep Learning 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-7162348","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516357826,"identity":"81de0a09-b56a-4706-88f6-ae057de01994","order_by":0,"name":"Zequan 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aeroengines","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":"Remaining Useful Life Prediction, Data Augment, Reinforcement Learning, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-7162348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7162348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prediction of remaining service life in complex aviation engine systems is of great significance for airlines to develop maintenance plans for engines and reduce maintenance cost.However, the complex operating conditions of the engine and insufficient fault mode data limit the prediction accuracy. One direction to solve such problems is data augmentation, which aims to generate synthetic data from real datasets to expand training samples and improve the model's generalization ability.Admittedly, There are already many mature data augmentation methods, but the optimal data augmentation strategy for RUL prediction tasks varies in different situations. Confirming which data augmentation strategy is most suitable for the current remaining useful life prediction problem requires human experience or extensive parameter experiments.This work proposes an automatic data augmentation method(AdaRUL),Build an automatic search space and use reinforcement learning algorithms to search for the optimal strategy in the automatic search space to expand the sample dataset. The experiments conducted on the C-MAPSS public dataset provided by NASA demonstrate that AdaRUL has successfully generated high fidelity multivariate monitoring data. In addition, these generated data effectively support RUL prediction tasks and significantly improve the predictive ability of underlying deep learning models.\u003c/p\u003e","manuscriptTitle":"An automatic data augment method for remaining useful life prediction of aeroengines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 12:00:25","doi":"10.21203/rs.3.rs-7162348/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":"53c1b9e4-17a3-4aa2-962e-bb5e7a1149ae","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T21:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 12:00:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7162348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7162348","identity":"rs-7162348","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}