A Functional Deep Learning Framework for Rotary Machines Fault Detection

A Functional Deep Learning Framework for Rotary Machines Fault Detection | 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 A Functional Deep Learning Framework for Rotary Machines Fault Detection Sayede Zohre Mousavi, Hossein Haghbin, Amin Torabi Jahromi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6710284/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Accurate and robust fault diagnosis of rotary machines is essential for maintaining the safety and reliability of industrial systems. Although deep learning methods have achieved remarkable success in this domain, their performance tends to decline in the presence of noise. In this study, a functional deep learning framework is introduced, in which raw vibration signals are transformed into smooth functional representations prior to being processed by deep learning architectures. By incorporating functional data analysis (FDA), the noise robustness and generalization ability of standard models are enhanced. Functional adaptations of several well-established one-dimensional convolutional networks are implemented and evaluated on a benchmark dataset under varying signalto-noise ratios (SNRs). The experimental results demonstrate that functional representations consistently improve diagnostic accuracy, particularly in lowSNR environments. These findings underscore the effectiveness of functional techniques in enhancing deep learning-based fault diagnosis of rotary machines operating under noisy conditions. Rotary machines fault diagnosis Functional data analysis Deep learning Vibration signals Condition monitoring Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 06 Oct, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 08 Jul, 2025 Editor invited by journal 08 Jun, 2025 First submitted to journal 20 May, 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. 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-6710284","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482514785,"identity":"c181f118-b569-4714-aaf4-baa6b6797c99","order_by":0,"name":"Sayede Zohre Mousavi","email":"","orcid":"","institution":"Persian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Sayede","middleName":"Zohre","lastName":"Mousavi","suffix":""},{"id":482514786,"identity":"97b0ac46-2f29-4e46-8352-31a53a661232","order_by":1,"name":"Hossein Haghbin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACAwYGNmYgKQcT4CFaizGpWhgYEhuIdpg5+/FrjwsK7qRv519jwPCjhkHGnJBmy56ccuMZBs9yd854Y8DYc4yBR+YAIYcdyEmT5jE4nLvhxhkDBt4GBh4JQg4zOP8GrCXdAKiF8S9RWm6kHwNpSTA432PATJwtN96wSc8wOGy4cwZbwWGZYxLEOCz9mXTBn8Py5vyHNz58U2NjT1ALMO4MIHolEhgOMDAQoYGBgf0BRAv/AWJUj4JRMApGwUgEAMnmPDigCi83AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8416-2354","institution":"Persian Gulf University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Haghbin","suffix":""},{"id":482514787,"identity":"5d3dd0cd-730f-4ffd-a44c-2fd7c170a8b3","order_by":2,"name":"Amin Torabi Jahromi","email":"","orcid":"","institution":"Persian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"Torabi","lastName":"Jahromi","suffix":""}],"badges":[],"createdAt":"2025-05-20 18:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6710284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6710284/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86381754,"identity":"81720704-e32d-4396-aa36-277e505525b1","added_by":"auto","created_at":"2025-07-10 04:23:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990650,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6710284/v1_covered_b1e1d1c4-4e0d-4946-b37d-3fdd3bbc95fa.pdf"}],"financialInterests":"","formattedTitle":"A Functional Deep Learning Framework for Rotary Machines Fault Detection","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rotary machines fault diagnosis, Functional data analysis, Deep learning, Vibration signals, Condition monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6710284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6710284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate and robust fault diagnosis of rotary machines is essential for maintaining the safety and reliability of industrial systems. 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