A Damage Identification Method for Wind Turbine Blade Fatigue Testing Based on Acoustic Emission Signals

A Damage Identification Method for Wind Turbine Blade Fatigue Testing Based on Acoustic Emission Signals | 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 A Damage Identification Method for Wind Turbine Blade Fatigue Testing Based on Acoustic Emission Signals Sun Shouxiang, Wang Jinghua, Zhang Xingjie, Zhang Leian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6974773/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 9 You are reading this latest preprint version Abstract Wind turbine blades are prone to various types of damage under long-term fatigue loading, making accurate damage type identification critical for structural health monitoring and operation and maintenance decision-making. This study proposes a hybrid algorithm framework that integrates SOM, Principal Component Analysis (PCA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Random Forest (RF) to identify damage in wind turbine blades based on acoustic emission (AE) signals collected during fatigue testing. Firstly, nonlinear and linear dimensionality reduction is performed on the raw AE features using SOM and PCA, respectively, resulting in six representative feature parameters. Then, DBSCAN is employed to cluster and label the reduced-dimension samples, enabling unsupervised signal classification without requiring prior knowledge. Based on the clustering results, a Random Forest model is trained and evaluated in a supervised manner, with classification accuracy, F1-score, and generalization performance quantitatively assessed. Experimental results show that the proposed method achieves over 90% accuracy in a four-class classification task, significantly outperforming traditional methods in both precision and stability. The clustering process exhibits strong robustness and is suitable for monitoring damage evolution at various stages of fatigue for the blade. This study provides an efficient and scalable signal processing approach for damage identification in composite wind turbine blades, laying a methodological foundation for intelligent and automated AE-based monitoring systems. Wind turbine blade Acoustic emission Damage identification Clustering analysis Random forest Structural health monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 22 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 25 Jun, 2025 First submitted to journal 25 Jun, 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-6974773","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514192529,"identity":"769e7366-0e80-45c4-babc-a85769f3a024","order_by":0,"name":"Sun Shouxiang","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Shouxiang","suffix":""},{"id":514192531,"identity":"ee198712-0092-41f5-a74f-5a0e2354f08d","order_by":1,"name":"Wang Jinghua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvmDjQ8+GNjY2bf3P3yQ8MOGsBY+CebDhjMq0pINeM4wGzzsSSOsRU6CLU2Y58xhxg0SOWySD9gOE+Ew6R4zZt62w8zmPGePVSTwHGbgb+9OwK9F5ozZw7lt6XyW7X1pNxIs0hkkzpzdgF8LQ465wds2a2aGMwfMbiTwWDMYSOQS1GImwdvGzNhwI8GsIIGNmQgtEmlpkjxnnBk33MgxY0hgcyZCC89hSCBL9hxLlkjsSeMh6Bf59kZIVPKzNx/8+OOHjRx/ey9+LRiAhzTlo2AUjIJRMAqwAgCdcUuLDbfeiwAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Wang","middleName":"","lastName":"Jinghua","suffix":""},{"id":514192532,"identity":"75b9838f-4ff9-46c8-bf9a-0465d461db51","order_by":2,"name":"Zhang Xingjie","email":"","orcid":"","institution":"Shandong Sinocera Functional Material Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Xingjie","suffix":""},{"id":514192536,"identity":"d6a6d11c-8cad-4114-a1cd-fe30d8144a76","order_by":3,"name":"Zhang Leian","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Leian","suffix":""}],"badges":[],"createdAt":"2025-06-25 12:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6974773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6974773/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-026-01334-w","type":"published","date":"2026-01-23T15:58:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101152966,"identity":"ae6e2976-ca8a-44ba-a654-e887bd93101d","added_by":"auto","created_at":"2026-01-26 16:13:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1465932,"visible":true,"origin":"","legend":"","description":"","filename":"ADamageIdentificationMethodforWindTurbineBladeFatigueTestingBasedonAcousticEmissionSignals.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6974773/v1_covered_d0a5fbf2-3471-48ba-bcc9-6b900c4953be.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Damage Identification Method for Wind Turbine Blade Fatigue Testing Based on Acoustic Emission Signals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wind turbine blade, Acoustic emission, Damage identification, Clustering analysis, Random forest, Structural health monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6974773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6974773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWind turbine blades are prone to various types of damage under long-term fatigue loading, making accurate damage type identification critical for structural health monitoring and operation and maintenance decision-making. 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