Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis

Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing 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 Research Article Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis Fengfeng Bie, Hanyang Zhang, Fengxia Lyu, Yi Lu, Jian Peng, Yi Miao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2606904/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 As a key component of mechanical system, the extraction and accurate identification of fault characteristic information of rolling bearing is very important to ensure its normal operation. The diagnosis accuracy is occasionally low due to the limitation of information collected by a single type of data source. In this paper, the bearing vibration signal and acoustic emission signal are employed as analysis sources, a novel method based on ICCEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) with optimized SVM (support vector machine) is presented for the fault information fusion, feature extraction, and fault pattern recognition of rolling bearing. Firstly, ICEEMDAN algorithm is developed to decompose the rolling bearing vibration signal and acoustic emission signal for a series of IMF (intrinsic mode function) components. Secondly, the valuable components that can characterize the original signal status are selected based on the correlation coefficient-variance contribution criterion. Thirdly, the singular spectral entropy of the reconstructed component is calculated as the eigenvalue and the two signal eigenvectors are fused as a new eigenvector set. Finally, the feature vector set is input into the optimized SVM classifier model based on PSO optimization for training and pattern recognition, in which the accuracy and efficiency of the classifier model and SVM classifier model are compared. Study of model simulation and fault simulation experiments show that the presented model based on the singular value entropy fusion of ICEEMDAN and PSO-SVM can effectively extract the fault characteristics of rolling bearing signals and has a desired performance in the accurate pattern recognition. rolling bearing ICEEMDAN singular value entropy characteristic fusion PSO-SVM pattern recognition 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-2606904","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":178214529,"identity":"8bdee342-c9a2-440c-97c8-97d2155ac7ee","order_by":0,"name":"Fengfeng Bie","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fengfeng","middleName":"","lastName":"Bie","suffix":""},{"id":178214530,"identity":"5df2c8ab-8bf0-4ec0-b20a-9b091ec976f6","order_by":1,"name":"Hanyang Zhang","email":"","orcid":"","institution":"Changzhou 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The diagnosis accuracy is occasionally low due to the limitation of information collected by a single type of data source. In this paper, the bearing vibration signal and acoustic emission signal are employed as analysis sources, a novel method based on ICCEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) with optimized SVM (support vector machine) is presented for the fault information fusion, feature extraction, and fault pattern recognition of rolling bearing. Firstly, ICEEMDAN algorithm is developed to decompose the rolling bearing vibration signal and acoustic emission signal for a series of IMF (intrinsic mode function) components. Secondly, the valuable components that can characterize the original signal status are selected based on the correlation coefficient-variance contribution criterion. Thirdly, the singular spectral entropy of the reconstructed component is calculated as the eigenvalue and the two signal eigenvectors are fused as a new eigenvector set. Finally, the feature vector set is input into the optimized SVM classifier model based on PSO optimization for training and pattern recognition, in which the accuracy and efficiency of the classifier model and SVM classifier model are compared. Study of model simulation and fault simulation experiments show that the presented model based on the singular value entropy fusion of ICEEMDAN and PSO-SVM can effectively extract the fault characteristics of rolling bearing signals and has a desired performance in the accurate pattern recognition.\u003c/p\u003e","manuscriptTitle":"Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-24 09:11:53","doi":"10.21203/rs.3.rs-2606904/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":"e1524869-b911-4af3-9da3-96ac7fa10494","owner":[],"postedDate":"February 24th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T07:44:10+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-24 09:11:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2606904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2606904","identity":"rs-2606904","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}