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Several studies have used time- and frequency-domain features with machine learning to predict faults such as erosion and cracks, but an integrated unified framework remains limited. This paper proposes a feature-engineering workflow combining time- and frequency-domain vibration signals and machine learning classifiers to distinguish erosion and crack faults, using open-source experimental vibration data.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/classification-analytics-for-wind-turbine-blade-faults-integrated-signal-analysis-and-machine-learning-approach/290616/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/classification-analytics-for-wind-turbine-blade-faults-integrated-signal-analysis-and-machine-learning-approach/290616.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What fault types are targeted for classification in this study?","Question",{"text":113,"@type":114},"The study focuses on distinguishing erosion faults and crack faults in wind turbine blades using vibration signal features.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the time-frequency behavior differ between erosion and crack faults?",{"text":118,"@type":114},"Crack faults produce amplitude modulation that generates sidebands around the fundamental frequency, while erosion faults introduce asymmetry and flatness that lead to harmonics in the frequency-domain results.",{"name":120,"@type":111,"acceptedAnswer":121},"Why is combining time- and frequency-domain features important?",{"text":122,"@type":114},"Using both time-and frequency-fault features improves the performance of the machine learning algorithms, providing more reliable fault diagnostics for maintenance decisions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290616,1789642210,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Article  \nClassification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach  \nWaqar Ali *, Idriss El-Thalji , Knut Erik Teigen Giljarhus and Andreas Delimitis  \nCitation: Ali, W.; El-Thalji, I.;  \nGiljarhus, K.E.T.; Delimitis, A. Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach. Energies 2024, 17, 5856. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)en17235856  \nAcademic Editors: Filipe Magalhães and Sérgio Pereira  \nReceived: 26 October 2024  \nRevised: 18 November 2024  \nAccepted: 20 November 2024  \nPublished: 22 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Mechanical and Structural Engineering and Materials Science, University of Stavanger,  \n4021 Stavanger, Norway; [idriss.el-thalji@uis.no](idriss.el-thalji@uis.no) (I.E.-T.); [knut.e.giljarhus@uis.no](knut.e.giljarhus@uis.no) (K.E.T.G.);  \n[andreas.delimitis@uis.no](andreas.delimitis@uis.no) (A.D.)  \n* Correspondence: [ali.waqar@uis.no](ali.waqar@uis.no)  \nAbstract: Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time-and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time-and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time-and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events.  \nKeywords: predictive maintenance; machine learning; diagnostic analytics; erosion fault; crack fault; wind turbine blade  \n1. Introduction  \nWind energy is a rapidly growing sector within the renewable energy landscape, driven by the global demand for sustainable and clean energy sources [1] . The reliability and efficiency of wind energy systems heavily depend on the structural integrity of wind turbine blades. These blades are exposed to various environmental and operational stresses, which can lead to structural faults such as cracks and erosion [2] . The early detection and classification of such faults are crucial for preventing catastrophic f","cbCaitijvljsSHwm","https://ap.wps.com/l/cbCaitijvljsSHwm","pdf",2696100,27,"English","# Abstract\n# 1. Introduction\n# Keywords","[{\"question\":\"What fault types are targeted for classification in this study?\",\"answer\":\"The study focuses on distinguishing erosion faults and crack faults in wind turbine blades using vibration signal features.\"},{\"question\":\"How does the time-frequency behavior differ between erosion and crack faults?\",\"answer\":\"Crack faults produce amplitude modulation that generates sidebands around the fundamental frequency, while erosion faults introduce asymmetry and flatness that lead to harmonics in the frequency-domain results.\"},{\"question\":\"Why is combining time- and frequency-domain features important?\",\"answer\":\"Using both time-and frequency-fault features improves the performance of the machine learning algorithms, providing more reliable fault diagnostics for maintenance decisions.\"}]","Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach | PDF",68]