A Hybrid Hierarchical Health Monitoring Solution for Autonomous Detection, Localization and Quantification of Damage Sources in Composite Wind Turbine Blades

A Hybrid Hierarchical Health Monitoring Solution for Autonomous Detection, Localization and Quantification of Damage Sources in Composite Wind Turbine Blades | 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 A Hybrid Hierarchical Health Monitoring Solution for Autonomous Detection, Localization and Quantification of Damage Sources in Composite Wind Turbine Blades Nikhil Holsamudrkar, Shirsendu Sikdar, Akshay Prakash Kalgutkar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5224831/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Glass fiber reinforced polymer (GFRP) composites are widely used in windturbine blades due to their excellent strength-to-weight ratio and construction flexibilities.However, wind turbines often operate in harsh environmental conditionsthat can lead to various types of damage, including abrasion, corrosion, fractures,cracks, and delamination. Early detection through structural health monitoring(SHM) is essential for maintaining the efficient and reliable operation ofwind turbines, minimizing downtime and maintenance costs, and optimizing energyoutput. This paper presents a hybrid machine-learning model that leveragesacoustic emission (AE) data to identify and classify different types of damage inlaboratory-based composite wind turbine blades. The AE data is collected using asingle sensor, with damage simulated by artificial AE sources (Pencil lead break)and low-velocity impacts. Additionally, simulated abrasion on the blade’s leading edge resembles environmental wear. A deep learning-based hybrid hierarchicalframework is developed for damage classification, localization, and site assessment.This hybrid model offers superior accuracy and robustness compared to theconventional Convolutional Neural Network (CNN) models. The developed SHMsolution provides a more effective and practical solution for in-service monitoringof wind turbine blades, particularly in wind farm settings, with the potential forfuture wireless sensor applications. Physical sciences/Engineering Physical sciences/Engineering/Mechanical engineering Acoustic Emission Composites Damage Localization Deep learning Structural Health Monitoring Wind Turbine Blades Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Feb, 2025 Reviews received at journal 06 Feb, 2025 Reviewers agreed at journal 04 Feb, 2025 Reviews received at journal 09 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers invited by journal 29 Oct, 2024 Editor assigned by journal 28 Oct, 2024 Editor invited by journal 28 Oct, 2024 Submission checks completed at journal 24 Oct, 2024 First submitted to journal 08 Oct, 2024 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. 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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-5224831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":413457309,"identity":"9d1207f2-242a-4249-8e82-c8325143097d","order_by":0,"name":"Nikhil Holsamudrkar","email":"","orcid":"","institution":"Indian Institute of Technology Bombay","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Holsamudrkar","suffix":""},{"id":413457310,"identity":"ef6497b2-2d5a-47c8-a628-b805e3f94186","order_by":1,"name":"Shirsendu 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