A Novel Integrated Fault Diagnosis Method Based on Digital Twin

A Novel Integrated Fault Diagnosis Method Based on Digital Twin | 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 Novel Integrated Fault Diagnosis Method Based on Digital Twin XiangRui Hu, Linglin Liu, Zhengyu Quan, Jinguo Huang, Jing Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4705642/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 Fault diagnosis plays a crucial role in the actual production activities of enterprises. In recent years, with the development and popularization of Internet of Things (IoT) technology, the efficient acquisition and storage of actual production data have become possible, enabling data-driven methods based on deep learning to achieve remarkable results in the field of fault diagnosis. However, existing technologies still have issues, such as less consideration of the temporal information of fault occurrences and the imbalance between normal and fault data in production activities, which can affect the performance of fault diagnosis. To address these problems, this paper proposes a novel integrated fault diagnosis method, comprehensively considering data balance, feature extraction, and temporal information at the time of fault occurrence.This method is established based on two key processes: the creation of a dataset using Digital Twin technology and the development of an integrated fault diagnosis model (CNN-BLSTM-Attention). The virtual production data generated under various operating conditions through Digital Twin technology provide us with a rich set of sample data. The integrated fault diagnosis model processes the input data using a sliding window to consolidate feature and temporal information, enabling precise fault diagnosis. This paper addresses the issue of small sample fault diagnosis for screw press faults and validates the effectiveness of the proposed method in practical applications. Experimental results demonstrate that, compared to existing fault diagnosis methods, the proposed method reduces noise sensitivity and significantly improves fault diagnosis accuracy, highlighting its superiority. Fault Diagnosis Deep Learning Data-Driven Digital Twin Screw Press 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-4705642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332193755,"identity":"9bca75dd-5b07-4f71-97b0-9e9565eb008c","order_by":0,"name":"XiangRui Hu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"XiangRui","middleName":"","lastName":"Hu","suffix":""},{"id":332193756,"identity":"728a0355-4393-4219-a743-f222f8b02eed","order_by":1,"name":"Linglin Liu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Linglin","middleName":"","lastName":"Liu","suffix":""},{"id":332193757,"identity":"f2fe1a42-9ebf-4a23-9dbe-27eb7aabced0","order_by":2,"name":"Zhengyu Quan","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhengyu","middleName":"","lastName":"Quan","suffix":""},{"id":332193758,"identity":"f46f7c9f-2f35-426e-91c7-29b67e777075","order_by":3,"name":"Jinguo Huang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinguo","middleName":"","lastName":"Huang","suffix":""},{"id":332193759,"identity":"69165780-2d4e-47a9-a753-be9902e774a4","order_by":4,"name":"Jing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RoQoCMRjA8U8GZ5kO2x2KzzA4GIIvs5VZFIwXDCeKF9TuY1xTmzC4NLvxbMazGQzuTCa3KLh/GAz2g+0bgM/3gzUXZikpYELm85InMztp1IRT6Ec7pWipCweS1gQgpqmU0XWJHEjWUjc+VSIHzRKRBkCyNbdcrC0HnCqxRxt2EYcehPqcWwhmtCbHVBuiA6DhxJHkpzGbihWyk84Cx6UhMT1JCU6kizAzQx69hxxyXWDrW9pEx1X1HL6/8v5IZn2Sbb8TUxB+7rDteB2qXE75fD7fH/cCVCVLLMNM2scAAAAASUVORK5CYII=","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-08 12:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4705642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4705642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61819957,"identity":"f0648b8b-f81f-4ca6-9600-ac7f6965c6ae","added_by":"auto","created_at":"2024-08-06 01:33:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1040672,"visible":true,"origin":"","legend":"","description":"","filename":"ANovelIntegratedFaultDiagnosisMethodBasedonDigitalTwin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4705642/v1_covered_2d9b4b5f-6d90-4ce9-8fc1-7de64378d460.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Integrated Fault Diagnosis Method Based on Digital Twin","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":"Fault Diagnosis, Deep Learning, Data-Driven, Digital Twin, Screw Press","lastPublishedDoi":"10.21203/rs.3.rs-4705642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4705642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFault diagnosis plays a crucial role in the actual production activities of enterprises. In recent years, with the development and popularization of Internet of Things (IoT) technology, the efficient acquisition and storage of actual production data have become possible, enabling data-driven methods based on deep learning to achieve remarkable results in the field of fault diagnosis. However, existing technologies still have issues, such as less consideration of the temporal information of fault occurrences and the imbalance between normal and fault data in production activities, which can affect the performance of fault diagnosis. To address these problems, this paper proposes a novel integrated fault diagnosis method, comprehensively considering data balance, feature extraction, and temporal information at the time of fault occurrence.This method is established based on two key processes: the creation of a dataset using Digital Twin technology and the development of an integrated fault diagnosis model (CNN-BLSTM-Attention). The virtual production data generated under various operating conditions through Digital Twin technology provide us with a rich set of sample data. The integrated fault diagnosis model processes the input data using a sliding window to consolidate feature and temporal information, enabling precise fault diagnosis. This paper addresses the issue of small sample fault diagnosis for screw press faults and validates the effectiveness of the proposed method in practical applications. 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