Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network

Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network | 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 Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network Yuan Xu, Wang Xiao, yang xiao, Qingfeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6303869/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jun, 2025 Read the published version in International Journal of Dynamics and Control → Version 1 posted You are reading this latest preprint version Abstract Bearing health state recognition is often affected by variable speeds and heavy load working conditions, making fault signal features difficult to identify and resulting in challenges in health condition recognition methods, including feature extraction difficulties and cross-device recognition challenges. This paper proposes the Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network. A multi-scale feature extraction method is developed, and a Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network is introduced. This network uses physical information layers and inverse physical information layers to constrain multi-scale features and adaptively adjust model hyperparameters, incorporating Inception multi-scale convolution and convolutional self-attention mechanisms to enhance feature recognition capabilities. To validate the effectiveness of the model, this paper constructs a Health Status Dynamic Time Warping-Mic Index and uses the Xi’an Jiao tong University bearing degradation dataset, PHM2012 challenge dataset, and centrifugal pump engineering data for model validation. The results demonstrate that the model performs well in recognizing the health status of equipment under cross-operating conditions and cross-device scenarios. Bearing health status evaluation Physics-Informed Neural Networks (PINN) Domain adaptation network (DAN) Inception Convolutional Block Attention Module (CBAM) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jun, 2025 Read the published version in International Journal of Dynamics and Control → 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. 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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-6303869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441044935,"identity":"880efbeb-41bc-4fc5-ae90-f14a47921708","order_by":0,"name":"Yuan Xu","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Xu","suffix":""},{"id":441044936,"identity":"b2c8bfad-3cab-4cf7-a76d-f99afc1a6a97","order_by":1,"name":"Wang Xiao","email":"","orcid":"","institution":"Western Branch of National Pipe Network Group United Pipeline Co","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xiao","suffix":""},{"id":441044940,"identity":"5707a425-090d-4564-81b3-81bf928f720a","order_by":2,"name":"yang xiao","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"yang","middleName":"","lastName":"xiao","suffix":""},{"id":441044942,"identity":"8f486bda-d8ef-4bd4-80e5-08b2416b4882","order_by":3,"name":"Qingfeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYNACGxtmfvbmAwc+/CBaS1oau2TPscSDM3uI13KY3+BGjvFhDjYiFBscP3v4xZsEZmmGAzkfDjPwMMjzix0goOVMXprlnAQ2Y8aGsxsOF1gwGM6cnYBfi9mBHDNj3h88ycyMvRsOz+BhSDC4TUjL+TdmxjwJEvVtzDwPDvOwEaMF6OvHPAkGzDxsPAzEabG/8caMcU5CArMED5sBMJAlCPtFsj/H+MObhP/M9vcfP/7w4YeNPL80AS1AwCbBg+BIEFQOAswfeAgrGgWjYBSMgpEMAKNkRxTJuVuJAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":true,"prefix":"","firstName":"Qingfeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-25 12:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6303869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6303869/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40435-025-01749-9","type":"published","date":"2025-06-10T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84726605,"identity":"291e9530-5753-430c-88f2-84870eaaae4c","added_by":"auto","created_at":"2025-06-16 16:07:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4846963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6303869/v1_covered_f3fdd15f-c0ae-447a-9fd8-257b07187f35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Bearing health status evaluation, Physics-Informed Neural Networks (PINN), Domain adaptation network (DAN), Inception, Convolutional Block Attention Module (CBAM)","lastPublishedDoi":"10.21203/rs.3.rs-6303869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6303869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Bearing health state recognition is often affected by variable speeds and heavy load working conditions, making fault signal features difficult to identify and resulting in challenges in health condition recognition methods, including feature extraction difficulties and cross-device recognition challenges. This paper proposes the Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network. A multi-scale feature extraction method is developed, and a Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network is introduced. This network uses physical information layers and inverse physical information layers to constrain multi-scale features and adaptively adjust model hyperparameters, incorporating Inception multi-scale convolution and convolutional self-attention mechanisms to enhance feature recognition capabilities. To validate the effectiveness of the model, this paper constructs a Health Status Dynamic Time Warping-Mic Index and uses the Xi’an Jiao tong University bearing degradation dataset, PHM2012 challenge dataset, and centrifugal pump engineering data for model validation. 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