DGLAConv-TasNet Motor Bearing Fault Sound Source Separation Network

DGLAConv-TasNet Motor Bearing Fault Sound Source Separation Network | 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 Article DGLAConv-TasNet Motor Bearing Fault Sound Source Separation Network Wenming Zhang, Zhenjiang Tian, Guanke Chen, Haibin Li, Tao Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8135333/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract In industrial production, achieving one-to-many audio monitoring and precise separation of overlapping motor sound sources is a crucial step in enhancing the efficiency of equipment fault diagnosis and ensuring the safe operation of production lines. To address the challenging task of separating individual motor sound sources from the audio of multiple motors operating simultaneously in industrial scenarios, we propose a novel end-to-end fully convolutional time-domain source separation network for motor sound source separation under complex interference conditions. This paper constructs a multipurpose acoustic fingerprint bearing fault dataset (HB-bearing fault dataset) tailored for real-world transportation scenarios. The main innovative contributions include: designing an enhancement module based on variational mode decomposition to avoid the issue of traditional de-noising methods treating the overall motor operation sound as noise suppression ; proposing a variable-depth time-domain convolutional network structure to address the mismatch between the original network’s receptive field and the motor pulse width; and designing an attention structure composed of local channel attention and local temporal attention to solve the problem of the network being prone to overfitting and difficult to generalize. Experiments show that the proposed method performs superiorly on the mixed fault task of the HB-bearing fault dataset. Physical sciences/Engineering Physical sciences/Mathematics and computing Audio source separation Bearing audio dataset Joint attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers invited by journal 22 Dec, 2025 Editor assigned by journal 02 Dec, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 27 Nov, 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-8135333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":564529742,"identity":"1d293d8b-3150-42fb-9a42-48b7d578ede9","order_by":0,"name":"Wenming Zhang","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Wenming","middleName":"","lastName":"Zhang","suffix":""},{"id":564529743,"identity":"04fd07c6-8491-4161-b67f-d5430db65044","order_by":1,"name":"Zhenjiang Tian","email":"","orcid":"","institution":"Yanshan 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To address the challenging task of separating individual motor sound sources from the audio of multiple motors operating simultaneously in industrial scenarios, we propose a novel end-to-end fully convolutional time-domain source separation network for motor sound source separation under complex interference conditions. This paper constructs a multipurpose acoustic fingerprint bearing fault dataset (HB-bearing fault dataset) tailored for real-world transportation scenarios. 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