Improving bearing fault diagnosis method based on the fusion of time- frequency diagram and a novel vision transformer

Improving bearing fault diagnosis method based on the fusion of time- frequency diagram and a novel vision transformer | 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 Improving bearing fault diagnosis method based on the fusion of time- frequency diagram and a novel vision transformer Jingyuan Wang, Yuan Zhao, Wenyan Wang, Ziheng Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5195341/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Bearings are indispensable components in mechanical equipment, it is crucial to realize accurate and reliable fault diagnosis of bearings. Traditional bearing fault diagnosis methods suffer from insufficient feature extraction and poor robustness. Consequently, this paper presents an improving bearing fault diagnosis method based on the fusion of time-frequency diagram and a novel vision transformer. On the one hand, the method adopts continuous wavelet transform to map the time-domain feature relationship of vibration onto the time-frequency domain. On the other hand, the method designs a novel vision transformer for bearing fault diagnosis model which can effectively improve the fault diagnosis performance and reduce the computational complexity on the basis of retaining the advantage of local feature extraction and dealing with long-range feature dependencies. In this paper, a new multi-head attention module called SRWA is designed to be utilized on the novel vision transformer model. Experiments are conducted to assess and analyze the performance of the proposed models using the bearing datasets: Case Western Reserve University data set and Harbin Institute of Technology inter-shaft bearing fault diagnosis data set. The experimental results demonstrate that the classification performance of the novel model put forward in this paper surpasses the state-of-the-art bearing fault diagnosis models on different datasets, even under variable operating conditions and noise conditions. Time-frequency diagram vision transformer attention module fault diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviews received at journal 30 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers agreed at journal 19 Oct, 2024 Reviewers invited by journal 19 Oct, 2024 Editor assigned by journal 04 Oct, 2024 Submission checks completed at journal 04 Oct, 2024 First submitted to journal 02 Oct, 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. 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-5195341","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374980888,"identity":"408a3b55-2122-40c9-9651-a2ae72a4a5b6","order_by":0,"name":"Jingyuan Wang","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Wang","suffix":""},{"id":374980889,"identity":"4c230a76-bbf0-44b0-87d7-8cf2bd342e2d","order_by":1,"name":"Yuan Zhao","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhao","suffix":""},{"id":374980890,"identity":"d7fccbcb-03d3-4f19-b54e-71f5eb664aed","order_by":2,"name":"Wenyan Wang","email":"","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenyan","middleName":"","lastName":"Wang","suffix":""},{"id":374980891,"identity":"5ea70cc9-a8b4-4f1a-9060-b309b277e0be","order_by":3,"name":"Ziheng Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFACxjYQKccgAeYxE6/FGKiFsYFILQxsICKxgWgtBucPtz34uKM2vX92j/kDhgrrxAb2swfwazlwsN1w5pnjuTPunDFsYDiTntjAk5eAV4vZwcY2ad62Y7kNN3IMGxjbDgNdyGOAX8thxjbpv23H0uXBWv4Ro+UYUAtjW02CAVhLAxFa7M8wtkn2th0w3HgjrXBGwrF04zaeHPxaJPuPP5P42VYnL3cjecOHDzXWsv3sZ/BrgYLDECqBARpNRIA6ItWNglEwCkbBiAQABo9KAZFlS0wAAAAASUVORK5CYII=","orcid":"","institution":"Anhui University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ziheng","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-10-03 01:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5195341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5195341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69404431,"identity":"04da52de-1c75-4a48-85f7-b212264f43c8","added_by":"auto","created_at":"2024-11-20 03:56:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":929381,"visible":true,"origin":"","legend":"","description":"","filename":"Improvingbearingfaultdiagnosismethod240928.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5195341/v1_covered_0153684b-d84b-4233-8442-04b729375e73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving bearing fault diagnosis method based on the fusion of time- frequency diagram and a novel vision transformer","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-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Time-frequency diagram, vision transformer, attention module, fault diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-5195341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5195341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBearings are indispensable components in mechanical equipment, it is crucial to realize accurate and reliable fault diagnosis of bearings. 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