Classification of roller defects and damages based on Rayleigh wave signals and power spectrum images with specific sampling rate

Classification of roller defects and damages based on Rayleigh wave signals and power spectrum images with specific sampling rate | 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 Classification of roller defects and damages based on Rayleigh wave signals and power spectrum images with specific sampling rate Yue Zhang, Zhiwei Liu, Rui Zhou, Weidong Shi, Yupeng Cao, Biao Xiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6910730/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 It is important to detect the defects and damages in the rollers and repair it since the defects and damages of rollers has a negative impact on the quality of the rolled products. Ultrasonic testing technology has the advantages of large detection depth, accurate defect localization, low cost, convenient use, fast speed, and harmless to the human body. This article proposes a deep learning classification method of defects and damages based on Rayleigh wave signals and power spectrum images with specific sampling rate, automatic identification of four common types of defects and damages is achieved by establishing end-to-end learning models. Firstly, an organic glass inclined block and clamping device were designed. In the experiment, time-domain signals were received on the right side of the defected or damaged samples, and signal data sets and power spectrum image data sets were established for signals with different sampling rates. Next, a defect and damage detection model is established based on a deep learning framework, which includes ResNet, GoogLeNet, DenseNet, and AlexNet with convolutional channels to extract signal features for classifying defects and damages. Finally, the performances of DenseNet models with different structures and depths are compared based on key indicators such as accuracy and training time. The experimental demonstrates that under higher sampling rate conditions, using the power spectrum images as data input yields better results than directly using Rayleigh wave signals. Moreover, for the power spectrum images of 5 M Rayleigh waves, using ResNet-18 to establish a deep learning model can achieve a classification accuracy of up to 0.998 and shorter training time. Rayleigh wave ResNet power spectrum images classification 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. 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-6910730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534985822,"identity":"4683cd6c-e9ad-48c9-be00-628594512882","order_by":0,"name":"Yue Zhang","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":534985823,"identity":"ebd85f07-03ef-4197-a953-187085ad2171","order_by":1,"name":"Zhiwei Liu","email":"","orcid":"","institution":"Nantong 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Ultrasonic testing technology has the advantages of large detection depth, accurate defect localization, low cost, convenient use, fast speed, and harmless to the human body. This article proposes a deep learning classification method of defects and damages based on Rayleigh wave signals and power spectrum images with specific sampling rate, automatic identification of four common types of defects and damages is achieved by establishing end-to-end learning models. Firstly, an organic glass inclined block and clamping device were designed. In the experiment, time-domain signals were received on the right side of the defected or damaged samples, and signal data sets and power spectrum image data sets were established for signals with different sampling rates. Next, a defect and damage detection model is established based on a deep learning framework, which includes ResNet, GoogLeNet, DenseNet, and AlexNet with convolutional channels to extract signal features for classifying defects and damages. Finally, the performances of DenseNet models with different structures and depths are compared based on key indicators such as accuracy and training time. The experimental demonstrates that under higher sampling rate conditions, using the power spectrum images as data input yields better results than directly using Rayleigh wave signals. 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