Fault diagnosis and location method of wind turbine bearing based on feature fusion under various working conditions1

Fault diagnosis and location method of wind turbine bearing based on feature fusion under various working conditions1 | 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 Fault diagnosis and location method of wind turbine bearing based on feature fusion under various working conditions 1 Jiang Zhuanping, Liao Hailong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7898653/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aiming at the problem of unbalanced fault data of rolling bearing and difficulty in extracting complementary features, a fault diagnosis and location method for wind turbine bearing based on feature fusion under various working conditions is proposed. This method takes the original vibration signal and current signal as the model input to avoid missed diagnosis and misdiagnosis caused by the insensitivity of vibration characteristics to faults. First, the Convolutional Block Attention Module (CBAM) is used to optimize the BilSTM for fault information timing feature extraction. At the same time, in order to solve the problem of manually setting 1DCNN super parameters, the goose swarm algorithm (GOA) is used to optimize 1DCNN for feature extraction, and the first level feature fusion is performed. Secondly, the one-dimensional fault data are transformed into a two-dimensional graph through the Markov Transition Field (MTF), and the 2DCNN is used to extract fault features, which are fused with the previously extracted fault features for secondary feature fusion. Finally, Softmax is used for fault classification. The experimental verification is carried out through the Paderborn dataset in Germany, and compared with the unoptimized 1DCNN, 2DCNN and BiLSTM to verify the superiority of the proposed method. Wind turbine bearing hierarchical fault diagnosis Goose optimization Feature fusion Markov transfer field Fault diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7898653","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545335626,"identity":"55450fef-bda2-46f2-9144-5f030c154fdf","order_by":0,"name":"Jiang 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conditions\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wind turbine bearing, hierarchical fault diagnosis, Goose optimization, Feature fusion, Markov transfer field, Fault diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7898653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7898653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problem of unbalanced fault data of rolling bearing and difficulty in extracting complementary features, a fault diagnosis and location method for wind turbine bearing based on feature fusion under various working conditions is proposed. This method takes the original vibration signal and current signal as the model input to avoid missed diagnosis and misdiagnosis caused by the insensitivity of vibration characteristics to faults. First, the Convolutional Block Attention Module (CBAM) is used to optimize the BilSTM for fault information timing feature extraction. At the same time, in order to solve the problem of manually setting 1DCNN super parameters, the goose swarm algorithm (GOA) is used to optimize 1DCNN for feature extraction, and the first level feature fusion is performed. Secondly, the one-dimensional fault data are transformed into a two-dimensional graph through the Markov Transition Field (MTF), and the 2DCNN is used to extract fault features, which are fused with the previously extracted fault features for secondary feature fusion. Finally, Softmax is used for fault classification. The experimental verification is carried out through the Paderborn dataset in Germany, and compared with the unoptimized 1DCNN, 2DCNN and BiLSTM to verify the superiority of the proposed method.\u003c/p\u003e","manuscriptTitle":"Fault diagnosis and location method of wind turbine bearing based on feature fusion under various working conditions1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 18:00:35","doi":"10.21203/rs.3.rs-7898653/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dde6e603-7a93-4d06-9d7e-73b9eba0e9cd","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T13:26:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 18:00:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7898653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7898653","identity":"rs-7898653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}