[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121784-en":3,"doc-seo-121784-105":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":4,"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},121784,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Fault detection of a wind turbine generator bearing using interpretable machine learning","Fault detection of a wind turbine generator bearing is developed using interpretable machine learning to support safer, more efficient condition-based maintenance. The approach leverages SCADA data to predict the operating temperature of a healthy bearing, then compares predicted and actual temperatures. Consistent abnormal deviations indicate degradation and potential bearing faults. The methodology is discussed with a case study and the model explanation is presented in detail to enable maintenance rescheduling based on detection outcomes.","TYPE Original Research PUBLISHED 13 December 2023 DOI 10.3389/fenrg.2023.1284676  \nOPEN ACCESS  \nEDITED BY  \nJuan P. Amezquita-Sanchez, Autonomous University of Queretaro, Mexico  \nREVIEWED BY  \nRyad Zemouri,  \nConservatoire National des Arts et Métiers (CNAM), France Marianne Rodgers,  \nWind Energy Institute of Canada, Canada  \n*CORRESPONDENCE  \nManeesh Singh,  \n [maneesh.singh@hvl. no](maneesh.singh@hvl. no)  \nRECEIVED 28 August 2023  \nACCEPTED 29 November 2023  \nPUBLISHED 13 December 2023  \nCITATION  \nBindingsbø OT, Singh M, Øvsthus K and Keprate A (2023), Fault detection of a wind turbine generator bearing using interpretable machine learning.  \nFront. Energy Res. 11:1284676 .  \ndoi: 10.3389/fenrg.2023.1284676  \nCOPYRIGHT  \n© 2023 Bindingsbø, Singh, Øvsthus and Keprate. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nFault detection of a wind turbine generator bearing using interpretable machine learning  \nOliver Trygve Bindingsbø 1, Maneesh Singh 1*, Knut Øvsthus 1 and Arvind Keprate 2  \n1Department of Mechanical and Marine Engineering, Western Norway University of Applied Sciences, Bergen, Norway, 2Department of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University, Oslo, Norway  \nIntroduction: During its operational lifetime, a wind turbine is subjected to a number of degradation mechanisms. If left unattended, the degradation of components will result in its suboptimal performance and eventual failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbine be regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally 6-month intervals) and the program (list of tasks) is prepared using experience or risk-reliability analysis, like Risk-based inspection (RBI) and Reliability-centered maintenance (RCM) . This time-based maintenance program can be improved upon by incorporating results from condition monitoring involving data collection using sensors and fault detection using data analytics. In order to properly carryout condition assessment, it is important to assure quality&quantity of data and to use correct procedures for interpretation of data for fault detection. This paper discusses the work carried out to develop a machine learning based methodology for detecting faults in a wind turbine generator bearing. Explanation of the working of the machine learning model has also been discussed in detail.  \nMethods: The methodology includes application of machine learning model using SCADA data for predicting operating temperature of a healthy bearing; and then comparing the predicted bearing temperature against the actual bearing temperature.  \nResults: Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing.  \nDiscussion: This fault detection can then be used for rescheduling the maintenance tasks. The working of this methodology is discussed in detail using a case study.  \nKEYWORDS  \nbearing, condition monitoring, fault detection, machine learning, offshore wind turbine, SCADA, SHAP  \n1 Introduction  \nIn order to meet the increasing demand for energy and yet reduce dependency on conventional fossil fuels, there has been a spurt in growth of wind farms (IEA, 2021) . These wind farms are comprised of arrays of wind turbines (typically horizontal), installed either onshore or offshore, to produce electricity from the wind. However, despite recent advances  \nFrontiers in Energy Research 01 [frontier","cbCaieK5flHTaPIj","https://ap.wps.com/l/cbCaieK5flHTaPIj","pdf",4552242,1,19,"English","en",105,"# Introduction\n# Methods\n## Condition monitoring and temperature prediction\n# Results\n# Discussion\n## Maintenance task rescheduling via detection","[{\"question\":\"How does the method detect faults in a wind turbine generator bearing?\",\"answer\":\"It uses SCADA data to predict the operating temperature of a healthy bearing and compares it with the actual bearing temperature. Abnormal differences consistently suggest degradation or a fault.\"},{\"question\":\"Why is interpretable machine learning important in this fault detection approach?\",\"answer\":\"The paper discusses the working and explanation of the machine learning model in detail, supporting understanding of how detection conclusions are reached.\"},{\"question\":\"How can fault detection results be used for maintenance planning?\",\"answer\":\"Detected faults and abnormal behavior can be used to reschedule maintenance tasks, improving the maintenance program beyond fixed-interval inspection.\"}]","Fault detection of a wind turbine generator bearing using interpretable machine learning | PDF",1785806831,48,{"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},"fault-detection-of-a-wind-turbine-generator-bearing-using-interpretable-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/fault-detection-of-a-wind-turbine-generator-bearing-using-interpretable-machine-learning/121784/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method detect faults in a wind turbine generator bearing?","Question",{"text":75,"@type":76},"It uses SCADA data to predict the operating temperature of a healthy bearing and compares it with the actual bearing temperature. Abnormal differences consistently suggest degradation or a fault.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is interpretable machine learning important in this fault detection approach?",{"text":80,"@type":76},"The paper discusses the working and explanation of the machine learning model in detail, supporting understanding of how detection conclusions are reached.",{"name":82,"@type":73,"acceptedAnswer":83},"How can fault detection results be used for maintenance planning?",{"text":84,"@type":76},"Detected faults and abnormal behavior can be used to reschedule maintenance tasks, improving the maintenance program beyond fixed-interval inspection.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]