Combined with wavelet time frequency analysis and lightweight deep convolutional networkintelligent recognition of coal rock acoustic emission signal

Combined with wavelet time frequency analysis and lightweight deep convolutional networkintelligent recognition of coal rock acoustic emission signal | 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 Combined with wavelet time frequency analysis and lightweight deep convolutional networkintelligent recognition of coal rock acoustic emission signal Weijian Liu, Zhongkai Peng, Shilei Zhen, Zhuangzhuang Wang, Xinbo Luan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7618168/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 During deep coal mining operations, coal-rock instability disasters frequently occur. However, real-time monitoring is constrained by challenges such as significant noise interference, low recognition accuracy of traditional methods, and insufficient computational efficiency of models. To address these issues, this paper proposes a lightweight acoustic emission signal recognition method based on the improved MobileNet V2 framework. First, Morlet wavelet transform is employed to construct acoustic emission time-frequency diagrams. Through energy entropy minimization criteria, scale parameters are optimized to compress 200 linear scales into 80 logarithmic distributed feature scales, enhancing high-frequency resolution to 0.01 seconds while effectively suppressing power frequency interference-induced spectral aliasing. Second, a channel mean fusion strategy compatible with single-channel inputs is designed, incorporating a dynamic expansion factor mechanism. This approach reduces feature expression capacity while decreasing shallow-layer module parameters by 33%, compressing overall parameters to 2.1 MB (a 38.2% reduction). In 30,000 coal-rock sample tests, the enhanced model achieved 94.0% classification accuracy with a single inference requiring only 14.3 ms. The accuracy decreased by merely 5.2% under high-noise conditions, demonstrating excellent real-time performance and robustness. Research findings indicate that this method significantly improves model lightweighting and adaptability to complex working conditions while maintaining precision, providing a viable technical solution for intelligent monitoring and early warning of coal-rock instability disasters in mines. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards acoustic emission time-frequency analysis lightweight convolutional network Full Text Additional Declarations No competing interests reported. Supplementary Files rawdata.zip 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7618168","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534711613,"identity":"f93458d9-54ad-48d9-b2d0-e8fcc5e4ef7b","order_by":0,"name":"Weijian Liu","email":"","orcid":"","institution":"Zhongyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weijian","middleName":"","lastName":"Liu","suffix":""},{"id":534711614,"identity":"4ae5cbb7-5e0b-46fe-8589-40a1d5b9c9db","order_by":1,"name":"Zhongkai 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However, real-time monitoring is constrained by challenges such as significant noise interference, low recognition accuracy of traditional methods, and insufficient computational efficiency of models. To address these issues, this paper proposes a lightweight acoustic emission signal recognition method based on the improved MobileNet V2 framework. First, Morlet wavelet transform is employed to construct acoustic emission time-frequency diagrams. Through energy entropy minimization criteria, scale parameters are optimized to compress 200 linear scales into 80 logarithmic distributed feature scales, enhancing high-frequency resolution to 0.01 seconds while effectively suppressing power frequency interference-induced spectral aliasing. Second, a channel mean fusion strategy compatible with single-channel inputs is designed, incorporating a dynamic expansion factor mechanism. This approach reduces feature expression capacity while decreasing shallow-layer module parameters by 33%, compressing overall parameters to 2.1 MB (a 38.2% reduction). In 30,000 coal-rock sample tests, the enhanced model achieved 94.0% classification accuracy with a single inference requiring only 14.3 ms. The accuracy decreased by merely 5.2% under high-noise conditions, demonstrating excellent real-time performance and robustness. 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