A Multidimensional Reliability-Enhanced Belief Rule Base Model for Fault Diagnosis

A Multidimensional Reliability-Enhanced Belief Rule Base Model for Fault Diagnosis | 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 A Multidimensional Reliability-Enhanced Belief Rule Base Model for Fault Diagnosis Boyu Liu, Ning Li, Zida Xia, Jiaqi Wu, Yingmei Li, Shutong Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9402902/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Fault diagnosis is essential for the safe and stable operation of industrial equipment and for improving maintenance efficiency. The belief rule base (BRB) is well suited to fault diagnosis under complex conditions because of its strengths in knowledge representation and uncertainty reasoning. However, traditional BRB still faces three limitations in multi-class diagnosis: rule-scale expansion, insufficient use of feature reliability during inference, and inadequate exploitation of output reliability from multiple submodels. To address these issues, this study proposes a reliability-enhanced one-versus-rest BRB method, termed RE-OvR-BRB. First, the OvR strategy decomposes the multi-class task into binary subtasks and constructs parallel BRB submodels to reduce rule-combination complexity. Second, sample-varying feature reliability is incorporated into matching calculation, rule activation, and evidence fusion to suppress the adverse effects of low-reliability inputs. Third, a decision fusion mechanism considering submodel output reliability is introduced to improve the robustness of multi-class decisions. Experiments on the CWRU bearing dataset and the UConn gear dataset show that the proposed method outperforms competing methods in classification accuracy, decision confidence, and noise robustness, demonstrating its effectiveness under complex industrial conditions. Physical sciences/Engineering Physical sciences/Mathematics and computing belief rule base fault diagnosis multi-class diagnosis reliability-aware modeling evidential reasoning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 18 Apr, 2026 Editor invited by journal 17 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 16 Apr, 2026 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-9402902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":629617963,"identity":"adcf2f06-0bd0-40b7-b7fe-a6a643ecd9bd","order_by":0,"name":"Boyu Liu","email":"","orcid":"","institution":"Harbin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Boyu","middleName":"","lastName":"Liu","suffix":""},{"id":629617964,"identity":"68fd5b6d-46f1-405b-9e0c-4376039d4dc1","order_by":1,"name":"Ning Li","email":"","orcid":"","institution":"Harbin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""},{"id":629617965,"identity":"cd798b61-09ea-4e84-95ae-3e2c7d1b5f17","order_by":2,"name":"Zida Xia","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Zida","middleName":"","lastName":"Xia","suffix":""},{"id":629617966,"identity":"a7e14906-2208-429d-8d64-2c709431ba84","order_by":3,"name":"Jiaqi Wu","email":"","orcid":"","institution":"Harbin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Wu","suffix":""},{"id":629617967,"identity":"21e1d859-f47a-4b37-8bfb-9fbb382904ed","order_by":4,"name":"Yingmei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPmYGhgMgBht7DwMzRCwBvxY2uBaeM8RqgbMkcojVws5jeLjg1zY5Psm3xz4X7jjMwM+eY8Dwcwc+h/EYHJ7Zd9uYTTovefbMM4cZJHveGDD2niGghbfndmKbdI4xM2/bYQaDGzkGzIxthLXUt0megWixJ0oLz4/bCWwSPFBbJAhqYSs4zNtw27CNB+iwmW3pPBJnnhUc7MWjhZ//8ObPPH9uy8u3Ax1W2GYtx9+evPHBTzxaGBg4DBiQncEDIg7g08DAwP6AgeEPfiWjYBSMglEwwgEA7+xIHjwU58gAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yingmei","middleName":"","lastName":"Li","suffix":""},{"id":629617968,"identity":"03c42072-a9eb-42fe-b9e5-9af2b0a8df9a","order_by":5,"name":"Shutong Zhao","email":"","orcid":"","institution":"Harbin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shutong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-04-13 10:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9402902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9402902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870447,"identity":"0b5c81ec-a316-4a7a-a1e3-cab476df3fc3","added_by":"auto","created_at":"2026-04-27 07:39:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1356431,"visible":true,"origin":"","legend":"","description":"","filename":"AMultidimensionalReliabilityEnhancedBeliefRuleBaseModelforFaultDiagnosis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9402902/v1_covered_c6dd5b34-8fab-4010-ae5b-2a4bdbc3149d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multidimensional Reliability-Enhanced Belief Rule Base Model for Fault Diagnosis","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":"belief rule base, fault diagnosis, multi-class diagnosis, reliability-aware modeling, evidential reasoning","lastPublishedDoi":"10.21203/rs.3.rs-9402902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9402902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFault diagnosis is essential for the safe and stable operation of industrial equipment and for improving maintenance efficiency. 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