Data-Scarce Compound Fault Diagnosis in Rotating Machinery Using Multi-Sensor Spectral Fusion and CycleGAN-Based Data Augmentation

Data-Scarce Compound Fault Diagnosis in Rotating Machinery Using Multi-Sensor Spectral Fusion and CycleGAN-Based Data Augmentation | 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 Data-Scarce Compound Fault Diagnosis in Rotating Machinery Using Multi-Sensor Spectral Fusion and CycleGAN-Based Data Augmentation Ayantha Senanayaka, Sungkwang Mun, Abdullah Al Mamun, Nayeon Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9410125/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Compound fault diagnosis in rotating machinery is challenging due to interactions among multiple components and the limited availability of labeled data. These challenges are further amplified under data imbalance conditions commonly encountered in practical manufacturing environments. To address this issue, this study proposes a data-driven framework that combines multi-sensor spectral feature fusion with Cycle Generative Adversarial Networks (CycleGANs) for compound fault diagnosis under data-scarce conditions. Multi-sensor time-domain signals acquired from acoustic and vibration sensors are first transformed into spectral representations and fused to capture cross-sensor fault characteristics. CycleGAN is then employed to generate realistic compound fault samples without requiring paired data, thereby reducing data scarcity and imbalance. A Convolutional Neural Network (CNN) is subsequently utilized for automated feature extraction and fault classification. Experimental validation on a rotating machinery fault simulator demonstrates improved diagnostic performance across multiple imbalance levels (0.5, 0.25, 0.1, and 0.05). The proposed framework consistently outperforms conventional approaches, including Support Vector Machines (SVMs), Neural Networks (NNs), and Synthetic Minority Over-sampling Technique (SMOTE)-based methods. The results demonstrate the effectiveness of the proposed approach for compound fault diagnosis and highlight its potential for predictive maintenance of rotating machinery in manufacturing systems. Compound fault diagnosis Multi-sensor fusion Spectral feature extraction CycleGAN Data imbalance Predictive maintenance Machinery fault detection Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revisions Needed 03 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 15 Apr, 2026 First submitted to journal 13 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-9410125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624308492,"identity":"07b393f9-9b16-4218-b460-b5da308ffa01","order_by":0,"name":"Ayantha Senanayaka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFADHgbGB2CGBJhDnBZmgwMMBqRpYZMgSgt//+FjD37usEvs7zn8rPrjjj9y/LMbGB+8bcOtReJGWrph75nkxBln28xuHDxjYCxx5wCz4Vw8Whhu8JhJ8LYxJzacZwBqaTNI3CCRwCbNi0eL/Pnz3yT/ttUnzj/P/q0AqKUeqIX9Nz4tBgdyQGYeTtxwtseMAaglwQBoCzM+LYY30sykZduOG288c6ZY4mybseGMG4nNknPO4dYid/7wM8m3bdWy886kb/xQ2SYnzz8j+eCHN2V4vA8Fjg0INmMDLlUowJ4oVaNgFIyCUTAyAQBoXFbDO0GJnQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-6255-3673","institution":"Tennessee Tech University","correspondingAuthor":true,"prefix":"","firstName":"Ayantha","middleName":"","lastName":"Senanayaka","suffix":""},{"id":624308493,"identity":"714bcbb7-efa6-4484-bad6-4b409bd51c23","order_by":1,"name":"Sungkwang Mun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sungkwang","middleName":"","lastName":"Mun","suffix":""},{"id":624308494,"identity":"817c29c3-f6f1-48a3-939a-49e06f1c9410","order_by":2,"name":"Abdullah Al Mamun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"Al","lastName":"Mamun","suffix":""},{"id":624308495,"identity":"a3f71c21-2de6-459b-9ddb-f64cfcbbdaeb","order_by":3,"name":"Nayeon Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nayeon","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-04-14 03:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9410125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9410125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107711971,"identity":"5e5b197b-1cd6-45b7-af59-68c74098b3f2","added_by":"auto","created_at":"2026-04-24 09:47:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046735,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9410125/v1_covered_0fdb58a3-7694-484f-8aba-595bf97761ec.pdf"}],"financialInterests":"","formattedTitle":"Data-Scarce Compound Fault Diagnosis in Rotating Machinery Using Multi-Sensor Spectral Fusion and CycleGAN-Based Data Augmentation","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":"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":"Compound fault diagnosis, Multi-sensor fusion, Spectral feature extraction, CycleGAN, Data imbalance, Predictive maintenance, Machinery fault detection","lastPublishedDoi":"10.21203/rs.3.rs-9410125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9410125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCompound fault diagnosis in rotating machinery is challenging due to interactions among multiple components and the limited availability of labeled data. These challenges are further amplified under data imbalance conditions commonly encountered in practical manufacturing environments. To address this issue, this study proposes a data-driven framework that combines multi-sensor spectral feature fusion with Cycle Generative Adversarial Networks (CycleGANs) for compound fault diagnosis under data-scarce conditions. Multi-sensor time-domain signals acquired from acoustic and vibration sensors are first transformed into spectral representations and fused to capture cross-sensor fault characteristics. CycleGAN is then employed to generate realistic compound fault samples without requiring paired data, thereby reducing data scarcity and imbalance. A Convolutional Neural Network (CNN) is subsequently utilized for automated feature extraction and fault classification. Experimental validation on a rotating machinery fault simulator demonstrates improved diagnostic performance across multiple imbalance levels (0.5, 0.25, 0.1, and 0.05). The proposed framework consistently outperforms conventional approaches, including Support Vector Machines (SVMs), Neural Networks (NNs), and Synthetic Minority Over-sampling Technique (SMOTE)-based methods. 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