[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120201-en":3,"doc-seo-120201-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120201,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Gearbox Anomaly Detection in Wind Turbines Using Classical Machine Learning","This master thesis develops a data-driven model to classify and predict anomalies in offshore wind turbine gearboxes using machine learning. It reviews the evolution of wind turbines, explains why offshore deployment increases operational and maintenance costs, and highlights drivetrain/gearbox components as major sources of failures and downtime, especially bearings and gears. The study examines typical gearbox malfunction modes and the role of operational condition monitoring, then builds a classification pipeline to separate healthy and damaged conditions. Hyperparameter optimization improves efficiency, and model accuracy is validated on measured vibratory signals from a reference gearbox to support future real-time monitoring under harsh conditions.","ACIT5900  \nMASTER THESIS  \nin  \nApplied Computer and Information Technology (ACIT)  \nMay 2024  \nRobotics and Control  \nGearbox Anomaly Detection In Wind Turbines Using Classical Machine  \nLearning  \nTran Cong Duy  \nDepartment of Computer Science Faculty of Technology, Art and Design  \nPreface  \nThis master thesis entitled ‘Gearbox Anomaly Detection in Wind Turbines Using Classical Machine Learning Approach’ was written as the final work of a Master of Science in Applied Computer and IT (OsloMet) in Oslo during the spring semester of 2024. My field of specialization is in Robotics and Cybernetics , and this thesis is part of a harmonized work at the Mechanical and Computer Science Departments at OsloMet. Given my previous studies in Cybernetics and a profound passion for marine resources, I was determined to write about a topic related to how the marine sector could contribute to fostering a more sustainable future across its diverse domains. During the previous summer of 2023, I was extremely lucky to work as a data analyst in the maritime department at Det Norske Veritas (DNV) . Returning to the university in the fall semester, I learned more about various machine learning applications in control theory. The professor in this subject, Prof. Arvind Keprate , a Professor in Condition Monitoring and Mechanics, educated and inspired me about machine learning and condition monitoring possibilities in offshore wind turbines. With all relevant premises, I chose to have Prof. Arvind as my supervisor for this thesis since the field of wind turbines had piqued my interest.  \nThis thesis is the fruitful outcome of many hard-working hours and several direction adaptations. The original intention was to investigate and build a model-based solution with MATLAB, which would then be utilized to accomplish prognostic techniques to condition monitoring for the engineering asset assessment of wind turbines. Nevertheless, it turned out that was not beneficial without the appropriate allowable schedule as my master thesis at OsloMet was in a short version. After a couple of weeks of literature research and insightful meetings with the supervisor and co-supervisor, this would require more meticulously prior planning to exchange knowledge on mathematics and mechanics respectively. Hence, the final goal was to delve into the existing techniques for detecting failures and monitoring the operational conditions of offshore wind turbines , which is further developed into a data-driven model to tackle the raised concern.  \nPlace and date: Oslo , 15th May 2024  \nName and Signature: Tran Cong Duy  \nABSTRACT  \nThe primary goal of this master thesis is to develop a data-driven model that can classify or predict the anomalies of wind turbines’ gearboxes by applying machine learning techniques. Firstly, the thesis reviews the development of wind turbines from the origin to the state-of-the-art. Owing to the tremendous demand for wind energy, the turbines move offshore and continue growing in size and number. Consequently, this would result in an increment of operational and maintenance costs , which simultaneously poses many challenges for the wind industry. From the literature review, studies highlighted the drivetrain and gearbox as being responsible for major failures and the downtime of wind turbines. In particular, bearings and gears are the root cause of the recorded damage. This thesis examines the most typical malfunctioning modes of the wind turbine gearbox. The importance and evolution of operational condition monitoring are covered. Subsequently, a data-driven approach is considered , and the possibilities of various experimentations are discussed. A machine learning pipeline with classification algorithms is developed to distinguish between healthy and damaged scenarios. Hyperparameter optimization is introduced and performed to improve the model’s efficiency. The accuracy of the methods applied in this thesis is verified with the actual healthy an","cbCaipkdzU4MK26Q","https://ap.wps.com/l/cbCaipkdzU4MK26Q","pdf",3862871,1,95,"English","en",105,"# Preface\n# Abstract\n# Acknowledgements\n# List of Abbreviations\n# List of Symbols","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"To develop a data-driven machine learning model that classifies and predicts anomalies in wind turbine gearboxes.\"},{\"question\":\"Which gearbox components are emphasized as major failure causes?\",\"answer\":\"Bearings and gears are identified as the root causes behind recorded damage and failures.\"},{\"question\":\"How is the proposed model validated?\",\"answer\":\"Accuracy is verified using actual healthy and damaged vibratory signals collected from a reference wind turbine gearbox.\"}]","Gearbox Anomaly Detection in Wind Turbines Using Classical Machine Learning | PDF",1785728685,239,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"gearbox-anomaly-detection-in-wind-turbines-using-classical-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/gearbox-anomaly-detection-in-wind-turbines-using-classical-machine-learning/120201/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the thesis?","Question",{"text":75,"@type":76},"To develop a data-driven machine learning model that classifies and predicts anomalies in wind turbine gearboxes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which gearbox components are emphasized as major failure causes?",{"text":80,"@type":76},"Bearings and gears are identified as the root causes behind recorded damage and failures.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed model validated?",{"text":84,"@type":76},"Accuracy is verified using actual healthy and damaged vibratory signals collected from a reference wind turbine gearbox.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]