Innovative Transformer-Driven Remaining Useful Life (RUL) Prediction Enhanced by Adaptive Multi-Scale Feature Engineering

Innovative Transformer-Driven Remaining Useful Life (RUL) Prediction Enhanced by Adaptive Multi-Scale Feature Engineering | 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 Innovative Transformer-Driven Remaining Useful Life (RUL) Prediction Enhanced by Adaptive Multi-Scale Feature Engineering Lydia Hsiao-Mei Lin, Fang-Kai Ting, Shu-Han Liao, Simon Hung-Yi Lu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8634746/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Mechanical equipment often undergoes remaining life prediction (RUL) to ensure reliable, efficient, and optimal performance. A vast amount of industrial measurement data can significantly enhance the effectiveness of data-driven methods for RUL prediction. This study utilizes the Transformer architecture to predict the RUL of the FEMTO-ST bearings dataset, generated from the PRONOSTIA platform, an experimental platform for accelerated bearing degradation testing. Th proposed method introduces four key improvements to the Dual Aspect Self-Attention Transformer (DAST) framework. It is called Multi-scale Feature DAST(MFDAST) and includes the following improvements: (1) multi-scale feature extraction for enhanced performance, (2) an advanced attention mechanism, (3) the use of a Health Index (HI) for precise degradation tracking, and (4) model optimization through a genetic algorithm. The results show that the study comprehensively analyzes global and local features within extensive datasets by augmenting the DAST model with a multi-scale feature encoder layer. This methodological advancement reduces RMSE by 50%, outperforming traditional Recurrent Neural Network (RNN) approaches. Remaining Useful Life prediction Transformer Multi-scale feature DAST FEMTO-ST Full Text Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 31 Jan, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 19 Jan, 2026 First submitted to journal 18 Jan, 2026 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. 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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-8634746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577514490,"identity":"b0fbc3ec-3452-4e7b-995d-94076f79877c","order_by":0,"name":"Lydia Hsiao-Mei Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lydia","middleName":"Hsiao-Mei","lastName":"Lin","suffix":""},{"id":577514491,"identity":"28eb4c14-1597-4ade-875f-42e5672f86ac","order_by":1,"name":"Fang-Kai 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A vast amount of industrial measurement data can significantly enhance the effectiveness of data-driven methods for RUL prediction. This study utilizes the Transformer architecture to predict the RUL of the FEMTO-ST bearings dataset, generated from the PRONOSTIA platform, an experimental platform for accelerated bearing degradation testing. Th proposed method introduces four key improvements to the Dual Aspect Self-Attention Transformer (DAST) framework. It is called Multi-scale Feature DAST(MFDAST) and includes the following improvements: (1) multi-scale feature extraction for enhanced performance, (2) an advanced attention mechanism, (3) the use of a Health Index (HI) for precise degradation tracking, and (4) model optimization through a genetic algorithm. The results show that the study comprehensively analyzes global and local features within extensive datasets by augmenting the DAST model with a multi-scale feature encoder layer. This methodological advancement reduces RMSE by 50%, outperforming traditional Recurrent Neural Network (RNN) approaches.\u003c/p\u003e","manuscriptTitle":"Innovative Transformer-Driven Remaining Useful Life (RUL) Prediction Enhanced by Adaptive Multi-Scale Feature Engineering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 08:02:00","doi":"10.21203/rs.3.rs-8634746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-01-31T09:13:26+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-26T00:51:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T13:42:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T03:09:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2026-01-18T22:47:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a2a481a7-a364-4593-9311-56e9e8079e8b","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:09:19+00:00","versionOfRecord":{"articleIdentity":"rs-8634746","link":"https://doi.org/10.1007/s00170-026-17963-4","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-04-02 16:00:01","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2026-01-22 08:02:00","video":"","vorDoi":"10.1007/s00170-026-17963-4","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17963-4","workflowStages":[]},"version":"v1","identity":"rs-8634746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8634746","identity":"rs-8634746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}