{"paper_id":"b7399351-cd4e-4103-8700-478aeff773eb","body_text":"Lightweight Multi-Candidate Frequency Detection Algorithm for Gearbox Fault Identification | 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 Lightweight Multi-Candidate Frequency Detection Algorithm for Gearbox Fault Identification Wenzheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9354341/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract To address the vulnerability of traditional gearbox fault diagnosis methods—where decisions based on a single spectral peak are easily affected by noise, resonance, and random impulses—this paper proposes a lightweight multi-candidate frequency detection algorithm for gearbox fault identification. Building on the classical SSA-EHPS-CYCBD pipeline, we introduce structural optimizations: first, singular spectrum analysis (SSA) is used for nonparametric denoising to improve the SNR of impulsive features; next, energy aggregation and median absolute deviation (MAD) thresholding yield a set of candidate frequencies, which are then verified one by one using cyclostationary blind deconvolution (CYCBD) and the ICS₂ metric. Simulation results show that under SNR = − 18 dB, the proposed method achieves main-frequency and dual-frequency detection rates of 98.0% and 92.5%, respectively—significantly outperforming conventional EHPS (86.7% and 63.3%); the false-alarm rate is also reduced from 28.1% to 9.8%. Moreover, with the number of candidates set to Top-3, the method strikes the best balance between detection performance and computational complexity. The approach demonstrates higher robustness and adaptability under strong noise and compound fault conditions, offering an effective new avenue for early gearbox fault identification. gearbox fault diagnosis singular spectrum analysis (SSA) multi-candidate frequency detection cyclostationary blind deconvolution (CYCBD) feature identification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 15 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 21 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9354341\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":637855522,\"identity\":\"6b5b2579-88d3-4569-aa24-b5aed734e63b\",\"order_by\":0,\"name\":\"Wenzheng Zhang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGElEQVRIiWNgGAWjYBACgwMMDMwghv0MIMH4xwZIMjcwMBwgQgsjWEtDGgMDGyNpWg4ToeV47+HPBRV37Jqlm589/LrjvJzB/cbGBx/OMMjzi2HXZ3bmXILxjDPPkttkjpkby565bWxwjLHZcMYNBsOZsxOwa7mRY5DM23Y4mUciwUxagu124oZjjG3SPB8YEgxu49By/43BYd5/h5MlJNK/AbWcI6zF/gaPYTNvw2E7A4kcM8mPbQegWm7g1mJ5JseYmefY4QSgljJphjPJxpLHEoF+OSOB0y8Gx88Yf+apOWxvPyN9m+SPCjs5vsOHDz74cMxGnl8auxYYSGwAEsw8CAEJvMrBfgIRjD8IqhsFo2AUjIKRCAA1tGmoJ4fMdwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"ANHUI SANLIAN UNIVERSITY\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Wenzheng\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-08 09:22:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9354341/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9354341/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109122970,\"identity\":\"ce7389e8-0935-46ca-81d1-1ceae51c2466\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 17:52:05\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2088689,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"article.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9354341/v1_covered_b8ba1e3a-6350-4786-9344-1afcab3de610.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Lightweight Multi-Candidate Frequency Detection Algorithm for Gearbox Fault Identification\",\"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\":\"info@researchsquare.com\",\"identity\":\"discover-computing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Discover Computing](https://link.springer.com/journal/10791)\",\"snPcode\":\"10791\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10791/3\",\"title\":\"Discover Computing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"gearbox fault diagnosis, singular spectrum analysis (SSA), multi-candidate frequency detection, cyclostationary blind deconvolution (CYCBD), feature identification\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9354341/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9354341/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eTo address the vulnerability of traditional gearbox fault diagnosis methods\\u0026mdash;where decisions based on a single spectral peak are easily affected by noise, resonance, and random impulses\\u0026mdash;this paper proposes a lightweight multi-candidate frequency detection algorithm for gearbox fault identification. Building on the classical SSA-EHPS-CYCBD pipeline, we introduce structural optimizations: first, singular spectrum analysis (SSA) is used for nonparametric denoising to improve the SNR of impulsive features; next, energy aggregation and median absolute deviation (MAD) thresholding yield a set of candidate frequencies, which are then verified one by one using cyclostationary blind deconvolution (CYCBD) and the ICS₂ metric. Simulation results show that under SNR\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;18 dB, the proposed method achieves main-frequency and dual-frequency detection rates of 98.0% and 92.5%, respectively\\u0026mdash;significantly outperforming conventional EHPS (86.7% and 63.3%); the false-alarm rate is also reduced from 28.1% to 9.8%. Moreover, with the number of candidates set to Top-3, the method strikes the best balance between detection performance and computational complexity. 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