Acoustic emission localization in composite overwrapped pressure vessels via a CNN-based approach

Acoustic emission localization in composite overwrapped pressure vessels via a CNN-based approach | 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 Acoustic emission localization in composite overwrapped pressure vessels via a CNN-based approach David Blaha, Denis Bogomolov, Sina Zolfagharysaravi, Nicola Testoni, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7704768/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 This study proposes a Convolutional Neural Network (CNN)-based approach for Acoustic Emission (AE) source localization in Composite Overwrapped Pressure Vessels (COPVs). The method employs a custom CNN trained on Time-Frequency Analysis (TFA) images generated via Continuous Wavelet Transform (CWT) of AE signals. To enhance localization performance, the architecture incorporates the Difference in Time of Arrival (DToA) extracted from multiple AE sensor channels. A 40.6-liter Type IV COPV, for which a dataset of Hsu-Nielsen signals and related CWT images was generated considering 40 different zones on the vessel, is considered as a case study. To evaluate the effectiveness of the proposed approach, a comparative analysis is performed against the conventional AE localization model for cylinders implemented in state-of-art commercially available systems. Results demonstrate that a minimal sensor setup, comprising two sensors of different types combined with DToA data, achieves an AE source localization accuracy of 87.9%, significantly outperforming the conventional method. The approach demonstrates the feasibility of accurate AE source localization without any knowledge on the wave propagation characteristics and with a limited number of sensors, offering a foundation for data-driven enhancements in AE-based monitoring of composite structures. The dataset of AE signals, documented and available to download, also contributes a valuable benchmark for future research in this domain. Acoustic Emission Damage Localization Convolution Neural Network Composites Pressure Vessel Full Text Additional Declarations No competing interests reported. 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. 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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-7704768","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525062724,"identity":"d6cfa56e-4099-406b-9997-30ee8487410a","order_by":0,"name":"David Blaha","email":"","orcid":"","institution":"Czech Technical University in Prague","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Blaha","suffix":""},{"id":525062728,"identity":"9b3b73b7-2880-479a-a565-c25809f9abd6","order_by":1,"name":"Denis 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