Bearing Fault Diagnosis Based on Cross-image Multi-Attention Mechanism

Bearing Fault Diagnosis Based on Cross-image Multi-Attention Mechanism | 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 Article Bearing Fault Diagnosis Based on Cross-image Multi-Attention Mechanism Yupeng Liu, Weinan Zheng, Ying Du, Yuehui Wang, Jian Jin, Miao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6451467/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Bearings are crucial components of rotating machinery, and fault diagnosis is essential for ensuring the safe operation of mechanical systems. Neural networks, commonly used in bearing fault diagnosis, are effective in extracting deep features from fault signals but often fail to emphasize critical information. We propose a fault diagnosis method that integrates a cross-image multi-attention mechanism with a residual neural network. The collected vibration signals are first preprocessed using VMD-GAF and then fed into the network for fault detection. The results demonstrate that the CIMAM-ResNet18 model significantly enhances the robustness of signal processing, achieving an accuracy of 98.00% when tested on the experimental platform. Physical sciences/Engineering Physical sciences/Engineering/Mechanical engineering Bearing Fault Diagnosis Multi-Attention Mechanism CIMAM-ResNet18 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor assigned by journal 23 Apr, 2025 Editor invited by journal 23 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 15 Apr, 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-6451467","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448070783,"identity":"5ea56b28-3fd1-4ccc-a1a0-6597594510f6","order_by":0,"name":"Yupeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNvnDBx9+/GPDwy//+ABxWvgk2JKNJRvSZIA4gTgtchI8ZhK8DYdsDBpyDIh0mHRbmoTkjgM8BgxnPt54w2Anp9tASIvM4cMWhWfu8Jgz9m62nMOQbGx2gJAWhrTEGxJsz3gsm3m3SfMwHEjcRlhLjoEED9thHoNjPM+I1CKRYyTB2wbUcoaHjUgtPMeSjSXOpPFIzmAztpxjQIRf5NubDz78UGFjzy/B/PDGmwo7OYJaUIAED5FRg6yFVB2jYBSMglEwIgAAEJA/wap8C3oAAAAASUVORK5CYII=","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yupeng","middleName":"","lastName":"Liu","suffix":""},{"id":448070784,"identity":"e8d913b7-49e2-437c-97eb-d331a82a3f52","order_by":1,"name":"Weinan Zheng","email":"","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Weinan","middleName":"","lastName":"Zheng","suffix":""},{"id":448070785,"identity":"590f1b13-1946-4c85-8521-c698f30d4bf0","order_by":2,"name":"Ying Du","email":"","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Du","suffix":""},{"id":448070786,"identity":"f1b18670-6c83-474f-881d-26c0953d9cff","order_by":3,"name":"Yuehui Wang","email":"","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuehui","middleName":"","lastName":"Wang","suffix":""},{"id":448070789,"identity":"f18e85e0-e2a3-41ba-ae80-99a1e78270a4","order_by":4,"name":"Jian Jin","email":"","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Jin","suffix":""},{"id":448070790,"identity":"b90b0261-df75-48cd-9354-a1ce81570b27","order_by":5,"name":"Miao Yu","email":"","orcid":"","institution":"Jilin University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-04-15 06:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6451467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6451467/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-07562-w","type":"published","date":"2025-07-01T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86179125,"identity":"9ba8026f-48e4-4ac9-ab72-b5187de96edb","added_by":"auto","created_at":"2025-07-07 16:16:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1450207,"visible":true,"origin":"","legend":"","description":"","filename":"BearingFaultDiagnosisBasedonCrossimageMultiAttentionMechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6451467/v1_covered_66f0103a-9bec-49f7-b084-e2a872ea7eab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bearing Fault Diagnosis Based on Cross-image Multi-Attention Mechanism","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bearing Fault Diagnosis, Multi-Attention Mechanism, CIMAM-ResNet18","lastPublishedDoi":"10.21203/rs.3.rs-6451467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6451467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBearings are crucial components of rotating machinery, and fault diagnosis is essential for ensuring the safe operation of mechanical systems. Neural networks, commonly used in bearing fault diagnosis, are effective in extracting deep features from fault signals but often fail to emphasize critical information. We propose a fault diagnosis method that integrates a cross-image multi-attention mechanism with a residual neural network. The collected vibration signals are first preprocessed using VMD-GAF and then fed into the network for fault detection. The results demonstrate that the CIMAM-ResNet18 model significantly enhances the robustness of signal processing, achieving an accuracy of 98.00% when tested on the experimental platform.\u003c/p\u003e","manuscriptTitle":"Bearing Fault Diagnosis Based on Cross-image Multi-Attention Mechanism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 05:30:02","doi":"10.21203/rs.3.rs-6451467/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-30T05:25:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T03:46:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T14:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106005681559402773186979344346706433750","date":"2025-04-23T08:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116769829841262559761498499041502425197","date":"2025-04-23T07:34:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319251017818780506142970682026462276302","date":"2025-04-23T06:59:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T06:38:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96602449631021286378623503057684390981","date":"2025-04-23T05:07:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169559928707876929485363371978501134533","date":"2025-04-23T04:34:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T04:31:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-23T04:25:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-23T04:16:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T11:15:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-15T06:14:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35d2fd83-c724-467d-8cb9-3d14e1088554","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47693365,"name":"Physical sciences/Engineering"},{"id":47693366,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2025-07-07T16:05:08+00:00","versionOfRecord":{"articleIdentity":"rs-6451467","link":"https://doi.org/10.1038/s41598-025-07562-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:03","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-28 05:30:02","video":"","vorDoi":"10.1038/s41598-025-07562-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-07562-w","workflowStages":[]},"version":"v1","identity":"rs-6451467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6451467","identity":"rs-6451467","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}