Rolling bearing fault detection of rotary machine using a novel adaptive sparse representation

Rolling bearing fault detection of rotary machine using a novel adaptive sparse representation | 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 Rolling bearing fault detection of rotary machine using a novel adaptive sparse representation Sun Yuanhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3565238/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Vibration signals of rolling bearings are often flooded by the noise and interference in early stages of failure. But extracting fault features from noisy signals effectively is a huge challenge. Sparse representation (SR)-based approaches have been used broadly to fault detection due to its stable performance and good anti-noise property. Nevertheless, its performance is very much dictated by the sparse regularization term and regularization parameter-setup. To overcome the existing drawback, an adaptive k-sparsity-based weighted generalized minimax concave (Ada-KWGMC) SR algorithm is proposed in this paper. Specifically, a weighted generalized minimax concave (WGMC) penalty is developed firstly for inducing the sparsity and anti-noise performance in Ada-KWGMC. Then an adaptive parameter setup approach has been put forward to make the regularization parameter free, thereby promoting the applicability of Ada-KWGMC. In this parameter setup method, the k-sparsity and the solution algorithm are integrated to set the regularization parameter adaptively without losing fault information. The diagnostic results of simulated signal and real fault signal shows that Ada-KWGMC has good performance in the fault detection. Bearing fault detection Sparse representation Weighted generalized minimax-concave (WGMC) penalty Adaptive regularization parameter-setup Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Reject after review 22 Apr, 2024 Reviewers invited by journal 21 Mar, 2024 Reviewers agreed at journal 07 Mar, 2024 Editor assigned by journal 06 Nov, 2023 First submitted to journal 05 Nov, 2023 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-3565238","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276965278,"identity":"15907e3b-4e80-4367-81f2-d54096cc1a9c","order_by":0,"name":"Sun Yuanhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACfvbmg49/VPyX42dvIFKLZM+xZGOGM8zGkj0HiNRicCPHTJqxhTnR4EYCsS47c8ZMurCBLYHh5uONNxhqbKIJ6mBsbyu2nrmDJ49xdlqxBcOxtNwGQlqYeQ5vvMF7RqKYWTrHTIKx4TBhLWwSCQYSvG0GiW2SZ4jUwiORYiTN25aQ2CPBQ6QWCZ5jyYYzzhwwluAB+iWBGL/YH28++OBDxQE5++NAT32osSGsBRkYSCSQohyihVQdo2AUjIJRMDIAAE5RQoKYsXaFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4908-2962","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Sun","middleName":"","lastName":"Yuanhang","suffix":""}],"badges":[],"createdAt":"2023-11-06 02:32:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3565238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3565238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52455200,"identity":"23ae5095-b23c-4ab9-be85-3ef4ec7922f7","added_by":"auto","created_at":"2024-03-11 19:50:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810503,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3565238/v1_covered_2a78bb4f-af94-4d90-bf98-4d45e22ad733.pdf"}],"financialInterests":"","formattedTitle":"Rolling bearing fault detection of rotary machine using a novel adaptive sparse representation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bearing fault detection, Sparse representation, Weighted generalized minimax-concave (WGMC) penalty, Adaptive regularization parameter-setup","lastPublishedDoi":"10.21203/rs.3.rs-3565238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3565238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVibration signals of rolling bearings are often flooded by the noise and interference in early stages of failure. But extracting fault features from noisy signals effectively is a huge challenge. Sparse representation (SR)-based approaches have been used broadly to fault detection due to its stable performance and good anti-noise property. Nevertheless, its performance is very much dictated by the sparse regularization term and regularization parameter-setup. To overcome the existing drawback, an adaptive k-sparsity-based weighted generalized minimax concave (Ada-KWGMC) SR algorithm is proposed in this paper. Specifically, a weighted generalized minimax concave (WGMC) penalty is developed firstly for inducing the sparsity and anti-noise performance in Ada-KWGMC. Then an adaptive parameter setup approach has been put forward to make the regularization parameter free, thereby promoting the applicability of Ada-KWGMC. In this parameter setup method, the k-sparsity and the solution algorithm are integrated to set the regularization parameter adaptively without losing fault information. The diagnostic results of simulated signal and real fault signal shows that Ada-KWGMC has good performance in the fault detection.\u003c/p\u003e","manuscriptTitle":"Rolling bearing fault detection of rotary machine using a novel adaptive sparse representation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:42:03","doi":"10.21203/rs.3.rs-3565238/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Reject after review","date":"2024-04-22T10:09:30+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-21T08:01:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-03-07T11:06:19+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-06T14:31:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nonlinear Dynamics","date":"2023-11-05T05:25:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1f2f8dee-1f3f-4342-8c81-4258ec2d3196","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T19:42:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 19:42:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3565238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3565238","identity":"rs-3565238","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}