Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural 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 Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network Changgui Xie, Xin Zhao, Yongli Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3845709/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 To address difficult recognition of vibration signals of heavy-load robots, a new median neural network pattern recognition method based on artificial neural network was proposed. This method builds the pattern recognition network model by using the median neurons and particle swarm optimization algorithm. In other words, the fault modes of heavy-load industrial robots are recognized quickly and accurately by using median and multiplication function as the aggregate function, and combining with the particle swarm backpropagation algorithm under the premise that there’s abnormal or fault eigenvalues in the network input mode. According to the simulation analysis and case verification, the proposed median neural network method can recognize fault modes of heavy-load robots well and it still and a smaller one recognition fault rate more traditional algorithms In below the complicated severe environment with noises. It shows remarkable recognition accuracy as well as relatively high compatibility and stability. This method not only has high theoretical value in pattern recognition of heavy-duty robots, but also possesses very important engineering practice significance in fault pattern recognition of rotary machines like ventilator and gas blower.The proposed method not only has high theoretical value in pattern recognition of heavy-duty robots, but there are also important,it has practical significance in engineering and other fields fault pattern recognition of rotating machinery such as ventilators and gas blowers. median neural network heavy-load robots pattern recognition particle swarm Full Text 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. 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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-3845709","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323777760,"identity":"ba0408f0-5f1b-43c3-9a59-ff4dd40bdc5d","order_by":0,"name":"Changgui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACZjACAcYGhoSKf3IwLpFaHpw5YExYCwMDQg3jw7YDiQ2ElBscZz74uaDmjt38iGSgw9jupM9v5z34gaHGJhqXFslmtmTpGceeJW+8ATQ/gedZ7obDfMkSDMfScnFZx8/MY8bMw3Y42XAGSIsEc+4GZh4DCcaGwzi1sDHzf2Pm+QfTYsCcLt/MY/wDnxagLWzMvG2H7eQlQFoSDicwHOYxw2sL0C/G0rx9hxMMeB4CtRxIM9wA1GKRgMcvBucPP/zM8+2wvXx7+gPGn/9s5OX7zxjf+FBjg1MLDCRuOMDA/gPOTSCgHATs5QkZOgpGwSgYBSMXAAC7HFedkb6u7wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4133-3015","institution":"Chongqing Vocational Institute of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Changgui","middleName":"","lastName":"Xie","suffix":""},{"id":323777761,"identity":"41c609fd-477c-4a23-8a80-54ac08494274","order_by":1,"name":"Xin Zhao","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhao","suffix":""},{"id":323777762,"identity":"9fbec6b9-b37f-4661-915a-9d3d41176fd6","order_by":2,"name":"Yongli Zhu","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Yongli","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-01-08 15:13:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3845709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3845709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63834623,"identity":"9661aadf-1313-4a1f-8ec3-68e70377ca6b","added_by":"auto","created_at":"2024-09-02 20:15:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":580253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptxiechanggui1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845709/v1_covered_4d2375e3-2fd5-4e19-a412-9ac5898c7c38.pdf"}],"financialInterests":"","formattedTitle":"Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network","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":"median neural network, heavy-load robots, pattern recognition, particle swarm","lastPublishedDoi":"10.21203/rs.3.rs-3845709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3845709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address difficult recognition of vibration signals of heavy-load robots, a new median neural network pattern recognition method based on artificial neural network was proposed. This method builds the pattern recognition network model by using the median neurons and particle swarm optimization algorithm. In other words, the fault modes of heavy-load industrial robots are recognized quickly and accurately by using median and multiplication function as the aggregate function, and combining with the particle swarm backpropagation algorithm under the premise that there\u0026rsquo;s abnormal or fault eigenvalues in the network input mode. According to the simulation analysis and case verification, the proposed median neural network method can recognize fault modes of heavy-load robots well and it still and a smaller one recognition fault rate more traditional algorithms In below the complicated severe environment with noises. It shows remarkable recognition accuracy as well as relatively high compatibility and stability. 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