A Robust AI-Driven Multisensory Framework for Bearing and Gear Fault Diagnosis Based on VMD, HES, and RFE

A Robust AI-Driven Multisensory Framework for Bearing and Gear Fault Diagnosis Based on VMD, HES, and RFE | 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 A Robust AI-Driven Multisensory Framework for Bearing and Gear Fault Diagnosis Based on VMD, HES, and RFE Abdel wahhab LOURARI, Tarak BENKEDJOUH This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8520272/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 In the field of signal analysis for machinery health monitoring and fault diagnosis, this paper presents a comprehensive methodology that combines Variational Mode Decomposition (VMD), Hilbert Envelope Spectrum (HSE), Recursive Feature Elimination (RFE), and advanced machine learning techniques. The primary goal is to establish a robust and precise approach for signal decomposition and feature extraction. Initially, VMD is used to decompose the signal into Intrinsic Mode Functions (IMFs). The HES of each IMF is then calculated, and the IMF with the highest Spearman coefficient correlation with the HES of the original signal is selected. Key indicators are computed from this selected IMF, and RFE is employed to identify the most relevant features. The methodology begins with VMD-based signal decomposition. The performance of each IMF is assessed by its correlation with the HSE of the original signal. The IMF with the highest Spearman coefficient is selected as the primary diagnostic feature. These indicators are standardized to ensure robustness and comparability. The standardized features are then used for fault diagnosis with various machine learning algorithms, including support vector machines, random forests, and discriminant analysis. The proposed methodology is validated using five datasets comprising three vibrational, one acoustic, and one electrical dataset. Experimental results demonstrate the effectiveness of the approach in accurately detecting and diagnosing faults, enhancing the reliability and maintenance efficiency of industrial machinery. Fault diagnosis Health Indicator Variational Mode Decomposition (VMD) Hilbert Envelop Spectrum (HSE) Condition monitoring Signal Analysis Recursive Feature Elimination Feature Extraction 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. 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-8520272","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570897545,"identity":"7c9da997-2186-4781-bc1d-e686d56c24dd","order_by":0,"name":"Abdel wahhab LOURARI","email":"data:image/png;base64,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","orcid":"","institution":"Ecole Militaire Polytechnique","correspondingAuthor":true,"prefix":"","firstName":"Abdel","middleName":"wahhab","lastName":"LOURARI","suffix":""},{"id":570897546,"identity":"8157df57-fb56-4227-9450-629de0a5545a","order_by":1,"name":"Tarak BENKEDJOUH","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tarak","middleName":"","lastName":"BENKEDJOUH","suffix":""}],"badges":[],"createdAt":"2026-01-05 10:38:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8520272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8520272/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99856972,"identity":"c8e71e35-1e60-480d-9eed-2e594bb94571","added_by":"auto","created_at":"2026-01-09 06:03:48","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6901,"visible":true,"origin":"","legend":"","description":"","filename":"jamtJAMTD2600070.xml","url":"https://assets-eu.researchsquare.com/files/rs-8520272/v1/ea42d512bba05b801589db9e.xml"},{"id":99856974,"identity":"71c58ed6-6138-4c44-8a26-4313559004ed","added_by":"auto","created_at":"2026-01-09 06:03:49","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":929,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTD2600070165796.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8520272/v1/99bc700b10f360ca81110bef.xml"},{"id":99856973,"identity":"36f6f181-2ee5-4881-b932-e9d3db41ff53","added_by":"auto","created_at":"2026-01-09 06:03:48","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":843,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTD2600070Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8520272/v1/6dab8bbc2b278e3a3193a7c9.xml"},{"id":102298646,"identity":"c531aaa8-aeb8-4419-909c-abc2cc52e780","added_by":"auto","created_at":"2026-02-10 10:55:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6239293,"visible":true,"origin":"","legend":"","description":"","filename":"LOURARIetal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8520272/v1_covered_0d51da4c-6353-4069-aa4d-d05e2cd7b1a7.pdf"}],"financialInterests":"","formattedTitle":"A Robust AI-Driven Multisensory Framework for Bearing and Gear Fault Diagnosis Based on VMD, HES, and RFE","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":"Fault diagnosis ,Health Indicator, Variational Mode Decomposition (VMD), Hilbert Envelop Spectrum (HSE), Condition monitoring, Signal Analysis, Recursive Feature Elimination, Feature Extraction","lastPublishedDoi":"10.21203/rs.3.rs-8520272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8520272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the field of signal analysis for machinery health monitoring and fault diagnosis, this paper presents a comprehensive methodology that combines Variational Mode Decomposition (VMD), Hilbert Envelope Spectrum (HSE), Recursive Feature Elimination (RFE), and advanced machine learning techniques. The primary goal is to establish a robust and precise approach for signal decomposition and feature extraction. Initially, VMD is used to decompose the signal into Intrinsic Mode Functions (IMFs). The HES of each IMF is then calculated, and the IMF with the highest Spearman coefficient correlation with the HES of the original signal is selected. Key indicators are computed from this selected IMF, and RFE is employed to identify the most relevant features. The methodology begins with VMD-based signal decomposition. The performance of each IMF is assessed by its correlation with the HSE of the original signal. The IMF with the highest Spearman coefficient is selected as the primary diagnostic feature. These indicators are standardized to ensure robustness and comparability. The standardized features are then used for fault diagnosis with various machine learning algorithms, including support vector machines, random forests, and discriminant analysis. The proposed methodology is validated using five datasets comprising three vibrational, one acoustic, and one electrical dataset. Experimental results demonstrate the effectiveness of the approach in accurately detecting and diagnosing faults, enhancing the reliability and maintenance efficiency of industrial machinery.","manuscriptTitle":"A Robust AI-Driven Multisensory Framework for Bearing and Gear Fault Diagnosis Based on VMD, HES, and RFE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 06:03:44","doi":"10.21203/rs.3.rs-8520272/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":"ba36a411-6e5f-4afa-b746-1e06316a4648","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T13:15:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 06:03:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8520272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8520272","identity":"rs-8520272","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}