A graph representation learning-based method for fault diagnosis of rotating machinery under time-varying speed conditions

A graph representation learning-based method for fault diagnosis of rotating machinery under time-varying speed conditions | 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 graph representation learning-based method for fault diagnosis of rotating machinery under time-varying speed conditions Sichao Sun, Xinyu Xia, Hua Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5428325/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Nonlinear Dynamics → Version 1 posted 10 You are reading this latest preprint version Abstract The health of rotating machinery is critical to the quality and efficiency of the manufacturing process. However, the existing intelligent fault diagnosis methods are mostly carried out under constant speed conditions, which makes it difficult to adapt to the variability and complexity of equipment speed with time in actual industrial scenarios. Based on graph learning and self-attention mechanism, this study proposes a novel fault diagnosis method for rotating machinery under time-varying speed conditions. Node feature information is extracted from raw vibration signals in multiple directions to construct spatial graph data. Then the spatial graph is transformed into embedded data, and the spatiotemporal nested graph containing time-varying fault information is built. After that, the graph convolutional attention interactive parallel network model is established. Combining the advantages of the graph convolutional network and the self-attention mechanism, the fault information contained in the graph is deeply mined to promote the model to identify the fault types correctly. The superiority of the proposed method is verified by two time-varying speeds fault diagnosis test data. Compared with other deep learning methods, this method can still achieve optimal diagnostic results even in the case of insufficient training samples. Rotating machinery fault diagnosis Time-varying speeds Spatiotemporal nested graph Graph convolution Self-attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 12 Jan, 2025 Reviews received at journal 14 Dec, 2024 Reviews received at journal 13 Dec, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 16 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 10 Nov, 2024 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-5428325","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380165018,"identity":"146401db-41b4-496d-8627-192b53ba2cdd","order_by":0,"name":"Sichao Sun","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Sichao","middleName":"","lastName":"Sun","suffix":""},{"id":380165019,"identity":"9f1632e2-6336-45d5-9456-90cf73969eab","order_by":1,"name":"Xinyu Xia","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Xia","suffix":""},{"id":380165020,"identity":"c8375053-af4d-4f99-a21a-bfa648a77aa3","order_by":2,"name":"Hua Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACAwYeIFnBwAziSJCg5QzJWhjbIBzitJjznz34mHdeHbvBAeaDt3kY7PIIarFsOJdszLuNjdngAFuyNQ9DcjFhhx3sMZPm3cYD1MJjJs3DcCCxgaCWw0CVvHMkgFr4vxGp5RhIS4MByBY2IrWc4TE2nHMsgVnyMJux5RyDZCK0nD9j+OBNTV0y3/HmhzfeVNgR1gIDyZDINCBWPRDYkaB2FIyCUTAKRhoAAOAnMb/xCwPCAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-11-11 02:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5428325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5428325/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11071-025-11151-x","type":"published","date":"2025-04-11T16:05:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80558542,"identity":"c8549da4-188a-462f-b171-e4db6e655969","added_by":"auto","created_at":"2025-04-14 16:14:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158700,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5428325/v1_covered_25ee01fc-55df-49db-9b5a-a6363821a5e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A graph representation learning-based method for fault diagnosis of rotating machinery under time-varying speed conditions","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":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rotating machinery fault diagnosis, Time-varying speeds, Spatiotemporal nested graph, Graph convolution, Self-attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-5428325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5428325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe health of rotating machinery is critical to the quality and efficiency of the manufacturing process. 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