Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer

Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer | 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 Article Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer Zhao Jiang, Wei Yu, Xiaoming Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6420121/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 18 You are reading this latest preprint version Abstract This paper proposes a domain adaptation feature transfer framework to address the challenge of data distribution discrepancies in cross-condition remaining useful life (RUL) prediction. The method integrates signal processing techniques (Hilbert-Huang transform) with deep denoising autoencoders (DAE) to extract degradation-sensitive features. Transfer Component Analysis (TCA) is subsequently applied to align feature distributions between source and target domains. Experimental results on the IEEE PHM 2012 bearing dataset demonstrate the superior performance of the proposed method over traditional statistical features and existing domain adaptation approaches, with root mean square error (RMSE) and mean absolute percentage error (MAPE) reduced by 21.3% and 39.1%, respectively. Further analysis validates the robustness of the method under noisy and small-sample scenarios, highlighting its potential for industrial applications such as wind turbine gearbox monitoring and aircraft engine maintenance. Physical sciences/Engineering/Mechanical engineering Physical sciences/Engineering Physical sciences/Mathematics and computing Domain adaptation Remaining useful life Transfer component analysis Hilbert-Huang transform Denoising autoencoders Full Text Additional Declarations No competing interests reported. Supplementary Files datasets.zip Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 29 Jul, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers invited by journal 05 May, 2025 Editor assigned by journal 05 May, 2025 Editor invited by journal 24 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 10 Apr, 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. 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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-6420121","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452572738,"identity":"96b7ab34-a81d-48fa-b7b1-c51dcc9510bd","order_by":0,"name":"Zhao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACxgYQyWbDw8/fQJqWNBnJGQdIsovtsI1BQwKRipn7j1988KHsPI8BwwHGDx9ziHLYmWLDGedu85gzNzBLztxGjJbGnjRp3rbbPJYNB9iYeYnS0swD0nKOx+BAArFa2tiPAbUcIEVLDw8z0C/JPJIzDjYT5xfD/uMPgSFmZ8/P33zww0eitDQAgxdqYQMR6oFAnoH9AXEqR8EoGAWjYOQCAO1gNVILyZh+AAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Yuexiu University","correspondingAuthor":true,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Jiang","suffix":""},{"id":452572739,"identity":"f6b63c93-23b2-4940-90bf-9dad06f32d0c","order_by":1,"name":"Wei Yu","email":"","orcid":"","institution":"Zhejiang Yuexiu University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yu","suffix":""},{"id":452572740,"identity":"7c89232d-970e-45f0-baab-dfdcf777bb39","order_by":2,"name":"Xiaoming Li","email":"","orcid":"","institution":"Zhejiang Yuexiu University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-10 12:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420121/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-49358-6","type":"published","date":"2026-04-22T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107927742,"identity":"a1ef6b87-39ea-4b0c-a959-3d114b0be895","added_by":"auto","created_at":"2026-04-27 16:03:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3751528,"visible":true,"origin":"","legend":"","description":"","filename":"Remainingusefullifepredictionacrossdifferentoperationalconditionsbasedondomainadaptationfeaturetransfer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420121/v1_covered_a72f7aa0-9f85-4748-bffa-16b92e31262a.pdf"},{"id":82220167,"identity":"bf79cddb-ac4a-48b4-974c-92b2d3244561","added_by":"auto","created_at":"2025-05-08 02:01:27","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13671390,"visible":true,"origin":"","legend":"","description":"","filename":"datasets.zip","url":"https://assets-eu.researchsquare.com/files/rs-6420121/v1/6bbc3cc8cc3e1779d74080e6.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remaining useful life prediction across different operational conditions based on domain adaptation feature transfer","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Domain adaptation, Remaining useful life, Transfer component analysis, Hilbert-Huang transform, Denoising autoencoders","lastPublishedDoi":"10.21203/rs.3.rs-6420121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a domain adaptation feature transfer framework to address the challenge of data distribution discrepancies in cross-condition remaining useful life (RUL) prediction. 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