An adaptive optimization EEMD method and its application in bearing fault detection

An adaptive optimization EEMD method and its application in bearing fault detection | 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 An adaptive optimization EEMD method and its application in bearing fault detection Xinming Liu, Wenzhuang Chen, Aikun Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2615109/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 Aiming at the optimization of two important parameters (white noise amplitude coefficient and set average number) in the set empirical mode decomposition (EEMD), an adaptive EEMD parameter optimization method is proposed. First of all, this paper extracts the corresponding amplitude of the high-frequency component of the signal through the energy value of the first eigenmode function, uses the relative mean square error to determine the corresponding amplitude of the low-frequency component of the signal, and establishes the optimal amplitude evaluation criteria based on the corresponding amplitude of the two; At the same time, in order to improve the calculation efficiency and reduce the influence of white noise, the energy value of the first modal component is used to determine the optimal average number of sets; Then, the effectiveness of the method in this paper is verified by simulation experiments; Finally, this method is applied to the extraction of bearing inner ring fault vibration signal. The results show that compared with the traditional EEMD method, this method can adaptively determine the noise amplitude and the set average number, and can more effectively identify the periodic fault components of the vibration signal. Ensemble empirical mode decomposition (EEMD) Adaptive Parameter optimum Mode mixing Fault components 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. 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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-2615109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":178567099,"identity":"93e1ee03-b831-4ea6-b809-fe14fbb56592","order_by":0,"name":"Xinming Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Liu","suffix":""},{"id":178567100,"identity":"ecbbacf5-e62f-43d6-ba40-aedb1af9eba2","order_by":1,"name":"Wenzhuang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACNvbmAwc+8EjwMDYcPkCcFj6eY4kPZ8hYyDA3HksgToucRI6xMY9NhQ178xkDIh3Gc8BMgidHgoe37czHG28Y7OR0GwhpYW9Ik5A4I8Ej2XN2s+UchmRjswOEbTkmYdgjwWM44+w2aR6GA4nbCGqRSGyTSPwnwWN//80zYrUkMxscAAfyGTYitfAcY3zYANZyzNhyjgERfpFv7/9w+A9PnT0wKh/eeFNhJ0dQCwqQ4CEyapC1kKpjFIyCUTAKRgQAAA1BQfCqdl8DAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wenzhuang","middleName":"","lastName":"Chen","suffix":""},{"id":178567101,"identity":"63f27462-4e38-43ca-bf21-a3e0de16ea83","order_by":2,"name":"Aikun Mao","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aikun","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2023-02-22 07:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2615109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2615109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33538327,"identity":"7ad4be92-e088-4ddb-b0de-fe6e67771416","added_by":"auto","created_at":"2023-02-28 06:22:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":638871,"visible":true,"origin":"","legend":"","description":"","filename":"AnAdaptiveOptimizationEEMDMethodandItsApplicationinBearingFaultDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2615109/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An adaptive optimization EEMD method and its application in bearing fault detection","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":"Ensemble empirical mode decomposition (EEMD) , Adaptive , Parameter optimum , Mode mixing , Fault components","lastPublishedDoi":"10.21203/rs.3.rs-2615109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2615109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the optimization of two important parameters (white noise amplitude coefficient and set average number) in the set empirical mode decomposition (EEMD), an adaptive EEMD parameter optimization method is proposed. First of all, this paper extracts the corresponding amplitude of the high-frequency component of the signal through the energy value of the first eigenmode function, uses the relative mean square error to determine the corresponding amplitude of the low-frequency component of the signal, and establishes the optimal amplitude evaluation criteria based on the corresponding amplitude of the two; At the same time, in order to improve the calculation efficiency and reduce the influence of white noise, the energy value of the first modal component is used to determine the optimal average number of sets; Then, the effectiveness of the method in this paper is verified by simulation experiments; Finally, this method is applied to the extraction of bearing inner ring fault vibration signal. The results show that compared with the traditional EEMD method, this method can adaptively determine the noise amplitude and the set average number, and can more effectively identify the periodic fault components of the vibration signal.\u003c/p\u003e","manuscriptTitle":"An adaptive optimization EEMD method and its application in bearing fault detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-28 06:22:39","doi":"10.21203/rs.3.rs-2615109/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9206bd33-1f88-4358-9abf-c482c523b0d0","owner":[],"postedDate":"February 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-02T09:59:46+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-28 06:22:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2615109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2615109","identity":"rs-2615109","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}