Federated hybrid ARIMAX-LSTM for Collaborative Fan Fault Prognostics: A Cement Plant Case Study

Federated hybrid ARIMAX-LSTM for Collaborative Fan Fault Prognostics: A Cement Plant Case Study | 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 Federated hybrid ARIMAX-LSTM for Collaborative Fan Fault Prognostics: A Cement Plant Case Study Noureddine ALLASSAK, Salima TRICHNI, Fouzia OMARY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7033228/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 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 Ensuring the reliability of critical industrial assets is essential in Industry 4.0. However, centralized predictive maintenance (PM) approaches face major challenges related to data privacy and scalability across distributed sites. To address these limitations, we propose a novel fault prognostics framework that integrates a hybrid ARIMAX–Long Short-Term Memory (LSTM) model within a Federated Learning (FL) architecture. The ARIMAX component models linear dependencies and exogenous effects, while the LSTM captures nonlinear residual patterns. Importantly, only the LSTM parameters are collaboratively trained using FL, ensuring that raw operational data remains local. Experiments conducted on real-world data from a cement plant demonstrate strong predictive performance—evidenced by low MSE and MAE values and high R2 scores—along with stable convergence across clients. This work demonstrates the effectiveness of FL for privacy-preserving, scalable, and accurate time-series-based PM in complex industrial settings. Federated Learning Predictive Maintenance Fault Prognostics ARIMAX-LSTM Time Series Forecasting Data Privacy Full Text Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 15 Dec, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 03 Jul, 2025 Editor assigned by journal 03 Jul, 2025 First submitted to journal 02 Jul, 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. 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. 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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-7033228","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480217445,"identity":"c8fbbc38-ae01-4f42-88ed-c750baeb8841","order_by":0,"name":"Noureddine ALLASSAK","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-9345-4789","institution":"Mohammed V University of Rabat Faculty of Sciences: Universite Mohammed V de Rabat Faculte des Sciences","correspondingAuthor":true,"prefix":"","firstName":"Noureddine","middleName":"","lastName":"ALLASSAK","suffix":""},{"id":480217446,"identity":"efd65a56-5904-490c-874c-dfcaa2cd6e93","order_by":1,"name":"Salima TRICHNI","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Salima","middleName":"","lastName":"TRICHNI","suffix":""},{"id":480217447,"identity":"a4efa091-e91f-4076-8c1d-6f59fadadee9","order_by":2,"name":"Fouzia OMARY","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fouzia","middleName":"","lastName":"OMARY","suffix":""}],"badges":[],"createdAt":"2025-07-03 01:39:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7033228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7033228/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-17338-1","type":"published","date":"2026-01-06T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069321,"identity":"2a2eec06-86c4-41c2-8450-0f7e51caac6a","added_by":"auto","created_at":"2026-01-12 16:12:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1733524,"visible":true,"origin":"","legend":"","description":"","filename":"FederatedARIMAXLSTMforCollaborativeFanFault.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7033228/v1_covered_825a6874-e882-47cd-a1d7-b55f6cea4ad8.pdf"}],"financialInterests":"","formattedTitle":"Federated hybrid ARIMAX-LSTM for Collaborative Fan Fault Prognostics: A Cement Plant Case Study","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":"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},"keywords":"Federated Learning, Predictive Maintenance, Fault Prognostics, ARIMAX-LSTM, Time Series Forecasting, Data Privacy","lastPublishedDoi":"10.21203/rs.3.rs-7033228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7033228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Ensuring the reliability of critical industrial assets is essential in Industry 4.0. 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