A hybrid approach for gearbox fault diagnosis based on deep learning techniques

A hybrid approach for gearbox fault diagnosis based on deep learning techniques | 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 hybrid approach for gearbox fault diagnosis based on deep learning techniques Mokrane Bessaoudi, Houssem Habbouche, Tarak Benkedjouh, Ammar Mesloub This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3955773/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Faults identification plays a vital role in improving the safety and reliability of industrial machinery. Deep learning has stepped into the scene as a promising approach for detecting faults, showcasing impressive performance in this regard. However, challenges such as noise and variable working conditions often limit the effectiveness of these approaches. This study addresses these limitations by employing a combination of signal processing methods and neural networks. Specifically, the proposed methodology incorporates maximum overlapping discrete wavelet packet decomposition (MODWPD) for raw vibratory signal, mel frequency cepstral coefficient mapping (MFCC) for time-frequency feature extraction, and a fusion of bidirectional long and short-term memory network with convolutional neural networks (CNN-BiLSTM) to capture local features and temporal dependencies in sequential data. The evaluation is conducted using two diverse experimental datasets, PHM2009 for mixed defects and Case Western Reserve University (CWRU) for bearing faults, under unexpected operating conditions. The proposed method is rigorously tested through stratified K-fold cross-validation, demonstrating superior performance compared to a leading state-of-the-art model. Gearbox fault diagnosis Maximum overlapping discrete packet wavelet decomposition Mel frequency cepstral coefficient mapping Convolution neural networks Bidirectional long and short-term memory Full Text Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2024 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 29 Apr, 2024 Reviewers agreed at journal 21 Feb, 2024 Reviewers invited by journal 21 Feb, 2024 Editor assigned by journal 20 Feb, 2024 First submitted to journal 19 Feb, 2024 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. 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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-3955773","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274373067,"identity":"47e70988-2e84-4522-bb19-428aee8e6067","order_by":0,"name":"Mokrane Bessaoudi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mokrane","middleName":"","lastName":"Bessaoudi","suffix":""},{"id":274373068,"identity":"eb46a19d-6c0e-4b05-b955-dd1e045760e6","order_by":1,"name":"Houssem Habbouche","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Houssem","middleName":"","lastName":"Habbouche","suffix":""},{"id":274373069,"identity":"2ca9fa52-3c05-497a-a7a9-0470df8db138","order_by":2,"name":"Tarak Benkedjouh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAx4wlSAHIg88IE4LM1iLMVhLAilaEhvAFDFazHnOH/xcUZGWPj/s8EOgLXZyug0EtFj2NjNLnjmTk7vxdpoBUEuysdkBQg47z8wg2dhWkbtxdgJIy4HEbURoYf7Z+K8i3XB2+gcitZxtZpNsbMhJkJfOIdIWy57DZpYNx9IMN0jnFBxIMCDCL+Y8iY9vNtQky8vPTt/84UOFnRxBLQgXglUaEKscBOQbSFE9CkbBKBgFIwoAAAzaRlEaz4GhAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0447-9106","institution":"Ecole Militaire Polytechnique","correspondingAuthor":true,"prefix":"","firstName":"Tarak","middleName":"","lastName":"Benkedjouh","suffix":""},{"id":274373070,"identity":"8487279f-213f-465a-be14-bdc45eb27f77","order_by":3,"name":"Ammar Mesloub","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ammar","middleName":"","lastName":"Mesloub","suffix":""}],"badges":[],"createdAt":"2024-02-14 09:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3955773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3955773/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-024-13873-5","type":"published","date":"2024-06-11T15:09:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58822825,"identity":"a73d6692-e4a4-4585-9d12-00b15df3ecae","added_by":"auto","created_at":"2024-06-21 16:48:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1007603,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTD2400816.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3955773/v1_covered_dab25891-5f06-4c99-94dd-7a617a8d8922.pdf"}],"financialInterests":"","formattedTitle":"A hybrid approach for gearbox fault diagnosis based on deep learning techniques","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gearbox, fault diagnosis, Maximum overlapping discrete packet wavelet decomposition, Mel frequency cepstral coefficient mapping, Convolution neural networks, Bidirectional long and short-term memory","lastPublishedDoi":"10.21203/rs.3.rs-3955773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3955773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFaults identification plays a vital role in improving the safety and reliability of industrial machinery. 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