Time Frequency Analysis of Elastic Wave PSO OMP for Defects in Flat Steel of Down Conductors

Time Frequency Analysis of Elastic Wave PSO OMP for Defects in Flat Steel of Down Conductors | 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 Time Frequency Analysis of Elastic Wave PSO OMP for Defects in Flat Steel of Down Conductors Jing Zhang, Wei Liu, Minghui Bao, Peng Yang, Xuhua Liu, Longhuan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5215059/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract This article proposes a method based on elastic wave data reconstruction and denoising to solve the problem of defect localization in grounding grid down conductor flat steel. Due to environmental factors, cross media propagation, and interference from excitation sources, noise is coupled into the original signal during data acquisition, seriously affecting signal processing. To solve this problem, this paper uses the Particle Swarm Optimization Orthogonal Matching Pursuit (PSO-OMP) algorithm to reconstruct the signal, significantly reducing noise. A detailed analysis of the computational cost was conducted using different parameters of PSO-OMP, and a comparison was made from the statistical data of reconstructed signals. Finally, the optimal parameters for the PSO-OMP algorithm were determined. At the same time, this article compared several different traditional denoising algorithms, AI denoising algorithm, and PSO-OMP reconstructed signals. Then calculate the correlation function of the reconstructed signal echo and apply a smooth pseudo Wigner Ville distribution for time-frequency analysis. This method can identify the time delay and corresponding frequency of defect signals. Finally, by combining time delay and distance between sensors, the defect location can be accurately calculated. Actual testing has shown that compared to data without denoising, the relative error of the defect location measured by this method is less than 10%. This method provides a cost-effective solution for defect detection in grounding systems, particularly for early-stage cracks (10–25 mm) in power infrastructure. Compared to traditional excavation methods, it reduces maintenance costs by over 90% and minimizes downtime. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology defect localization of flat steel conductor denoising PSO-OMP optimization algorithm SPWVD Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 06 Apr, 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-5215059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443036600,"identity":"473027b5-e0f6-403e-aee5-52a4e99bead5","order_by":0,"name":"Jing Zhang","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":443036603,"identity":"054c7ae0-8699-48bb-b324-dffe52509a1a","order_by":1,"name":"Wei Liu","email":"","orcid":"","institution":"Chongqing Electric Power Company Electric Power Science Research 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Due to environmental factors, cross media propagation, and interference from excitation sources, noise is coupled into the original signal during data acquisition, seriously affecting signal processing. To solve this problem, this paper uses the Particle Swarm Optimization Orthogonal Matching Pursuit (PSO-OMP) algorithm to reconstruct the signal, significantly reducing noise. A detailed analysis of the computational cost was conducted using different parameters of PSO-OMP, and a comparison was made from the statistical data of reconstructed signals. Finally, the optimal parameters for the PSO-OMP algorithm were determined. At the same time, this article compared several different traditional denoising algorithms, AI denoising algorithm, and PSO-OMP reconstructed signals. Then calculate the correlation function of the reconstructed signal echo and apply a smooth pseudo Wigner Ville distribution for time-frequency analysis. 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