Structured Sound Data Augmentation Based on Spectral Analysis for Machine Tool Operational State Monitoring

Structured Sound Data Augmentation Based on Spectral Analysis for Machine Tool Operational State Monitoring | 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 Structured Sound Data Augmentation Based on Spectral Analysis for Machine Tool Operational State Monitoring Yuseop Sim, Eunseob Kim, Hojun Lee, Junho Sohn, Martin Byung-Guk Jun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7575801/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Monitoring the operational state of manufacturing equipment is essential for improving productivity and ensuring process reliability in modern manufacturing. Sound has gained increasing attention as a non-contact and cost-effective solution for real-time monitoring. However, implementing sound-based monitoring using data-driven methods faces two critical challenges. First, machine sound is unstructured and highly context-dependent, making it difficult to isolate signals from individual components. Second, the limited diversity of curated datasets hinders the training of models that generalize across operational states. In machine tool operational state monitoring, to address these challenges, a two-fold approach is proposed: a structured warm-up cycle that enables isolation of sound signatures from key machine tool components, and a data augmentation pipeline that superposes interpolated component-wise signals to synthesize realistic multi-component sounds. Individual component signals are modeled as outputs of linear time-invariant (LTI) systems, and their frequency-domain representations are characterized using statistical features. This approximation enables physically meaningful modeling of component-wise sound behavior and supports scalable data synthesis. In data-scarce manufacturing settings, regression-based interpolation of spectral features enables the synthesis of component sounds under unseen conditions, such as varying spindle or axis speeds. This strategy expands the diversity of training data without requiring additional labeling. The proposed approach is applied to train a lightweight 1D convolutional neural network for multi-label classification of machine operations. Compared to conventional augmentation methods, it achieves a 26.2% improvement in macro F1 score, demonstrating the effectiveness of structured sound augmentation in enhancing model performance and generalizability. Sound augmentation Sound recognition Signal processing Convolutional neural network Full Text Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Accept as is for Publication 08 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 12 Sep, 2025 First submitted to journal 09 Sep, 2025 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-7575801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514206912,"identity":"bbb60af6-8ba0-47ee-b6f4-ef4da4b56b06","order_by":0,"name":"Yuseop Sim","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuseop","middleName":"","lastName":"Sim","suffix":""},{"id":514206913,"identity":"184a3f16-0e2a-41d1-a34a-f6db775f5bc2","order_by":1,"name":"Eunseob Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACCWYG5gcffvyTA3PAQgcIa2EznNlzwJgELUAszcN2ILGBaC2S7dwJBjw8d9K3S+Q+vM1TwSDHdyMBvxZpZt4NDyQsnuXunJFubM1zhsFYkpAWOaAWAwMe5twNN9LYpHnbGBI3EKNFIoGNOd0ArOUfQz1BLSCHSRxgO5wA0dLAAGQQ0CLZzLvNsLEnzXBnzzNmyznHJAxnnnmAX4vE+bObH//5YSNvzp7GeONNjY0833ECtsCBAdQIIpUjaRkFo2AUjIJRgAkAuYlCbtNkOqMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8391-944X","institution":"Purdue University","correspondingAuthor":true,"prefix":"","firstName":"Eunseob","middleName":"","lastName":"Kim","suffix":""},{"id":514206914,"identity":"40c47cc2-4a2b-487b-847e-55727a40b721","order_by":2,"name":"Hojun Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hojun","middleName":"","lastName":"Lee","suffix":""},{"id":514206915,"identity":"448df87f-c2c7-4c5e-9739-5852eb20653f","order_by":3,"name":"Junho Sohn","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junho","middleName":"","lastName":"Sohn","suffix":""},{"id":514206916,"identity":"40f00908-5655-413e-bc3a-1ba4f5e77666","order_by":4,"name":"Martin Byung-Guk Jun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"Byung-Guk","lastName":"Jun","suffix":""}],"badges":[],"createdAt":"2025-09-09 16:27:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7575801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7575801/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16797-w","type":"published","date":"2025-10-21T16:16:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490471,"identity":"42429579-66ba-4f69-98b5-09ca8697d278","added_by":"auto","created_at":"2025-10-27 17:10:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035056,"visible":true,"origin":"","legend":"","description":"","filename":"IJAMTmanufacript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7575801/v1_covered_74b178a6-93a1-407a-ae04-88786b9ae3f3.pdf"}],"financialInterests":"","formattedTitle":"Structured Sound Data Augmentation Based on Spectral Analysis for Machine Tool Operational State Monitoring","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":"Sound augmentation, Sound recognition, Signal processing, Convolutional neural network","lastPublishedDoi":"10.21203/rs.3.rs-7575801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7575801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Monitoring the operational state of manufacturing equipment is essential for improving productivity and ensuring process reliability in modern manufacturing. 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