Fault Diagnosis for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning

Fault Diagnosis for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning | 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 for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning Zejian Zhu, Weiming Wu, Tianrui Chen, Cong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1930103/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 In this paper a novel fault diagnosis (FD) approach combing the rein-forcement learning (RL) and the deterministic learning theory (DLT)is proposed for a class of discrete-time nonlinear system with unknowndynamics. First, a bank of DLT-based dynamical neural network (NN)identifiers are utilized to achieve locally-accurate approximations ofthe unknown system dynamics along the normal and fault trajecto-ries. Based on this, a novel feature learning method combing the RLwith DLT is proposed to further adapt the NN weights to extract dis-criminative features. The extracted features are represented by constant NNs. Finally, constant NN-based dynamical estimators are constructedto achieve rapid FD. The novelties of the proposed methods are: 1)according to the DLT, the exponential convergence of the NN weightscan be rigorously analysed based on the Lyapunov stability theory;2) a new class of strategic utility function is designed based on theconcept of the synchronization error in DLT-based dynamical pat-tern recognition approach, which is different from other RL-based FDtechniques that penalise the future wrong FD decisions. Simulationresults shows the practical significance of the proposed FD method. Reinforcement learning Deterministic learning Fault diagnosis Nonlinear systems 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-1930103","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":126875033,"identity":"359bfaf9-9b7d-454b-9d62-56908a2c4de7","order_by":0,"name":"Zejian Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zejian","middleName":"","lastName":"Zhu","suffix":""},{"id":126875034,"identity":"90aaf875-18d5-4ce2-afc9-831cf2869876","order_by":1,"name":"Weiming Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Wu","suffix":""},{"id":126875035,"identity":"35457e30-0078-4c9f-bfc2-dc5d0d71bd33","order_by":2,"name":"Tianrui Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tianrui","middleName":"","lastName":"Chen","suffix":""},{"id":126875036,"identity":"ad951767-e7ee-4994-b6bd-eeb6297e2896","order_by":3,"name":"Cong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDADfgYGAyDFTIIWyQaStRgcIFaLwfGzh1/8qLhjt/n84W0SDBXWiQ3sZw/g13ImL82y58yz5G030sokGM6kJzbw5CXg1WJ2IMfMmLHtcLLZDR4zCSAjsUGCxwC/lvNvIFqM+88AtfwjRsuNHOPHQC12Bgw5QC0NRGixv/HGjLHnzOEEiRtpxRYJx9KN23hy8GuR7M8x/vCj4rA9f//hjTc+1FjL9rOfwa8FCNgkgERiA4iZAOISUg8EzB9ADiRC4SgYBaNgFIxUAAAkgUdYtkhg8AAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Cong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2022-08-04 15:14:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1930103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1930103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24937316,"identity":"e5d6ff62-ca2c-4500-bf5f-50348ca18bd3","added_by":"auto","created_at":"2022-08-08 17:56:59","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1378599,"visible":true,"origin":"","legend":"","description":"","filename":"AIRSubmitFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1930103/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFault Diagnosis for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1930103/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Reinforcement learning, Deterministic learning, Fault diagnosis, Nonlinear systems","lastPublishedDoi":"10.21203/rs.3.rs-1930103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1930103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper a novel fault diagnosis (FD) approach combing the rein-forcement learning (RL) and the deterministic learning theory (DLT)is proposed for a class of discrete-time nonlinear system with unknowndynamics. First, a bank of DLT-based dynamical neural network (NN)identifiers are utilized to achieve locally-accurate approximations ofthe unknown system dynamics along the normal and fault trajecto-ries. Based on this, a novel feature learning method combing the RLwith DLT is proposed to further adapt the NN weights to extract dis-criminative features. The extracted features are represented by constant NNs. Finally, constant NN-based dynamical estimators are constructedto achieve rapid FD. The novelties of the proposed methods are: 1)according to the DLT, the exponential convergence of the NN weightscan be rigorously analysed based on the Lyapunov stability theory;2) a new class of strategic utility function is designed based on theconcept of the synchronization error in DLT-based dynamical pat-tern recognition approach, which is different from other RL-based FDtechniques that penalise the future wrong FD decisions. 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