[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119642-en":3,"doc-seo-119642-105":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},119642,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine learning methods for predictive maintenance using real-time data and time-frequency analysis - Diploma Thesis","This diploma thesis compares predictive maintenance techniques based on machine learning to estimate the Remaining Useful Life of a wind turbine shaft ball bearing. Classification algorithms, degradation models, and real-time updates are evaluated, including a Kalman Filter approach for continuous state refinement. The work first studies bearing failure mechanisms, the fundamentals of predictive maintenance, and machine learning methods. It then implements and assesses multiple prediction workflows, concluding with an efficiency comparison of each method.","UNIVERSITY OF WEST ATTICA UNIVERSIDAD DEL PAIS VASCO  \nFACULTY OF ENGINEERING FACULTY OF ENGINEERING VITORIA-GASTEIZ  \nDEPARTMENT OF MECHANICAL ENGINEERING DEPARTMENT OF MECHANICAL ENGINEERING  \nDiploma Thesis  \nMachine learning methods for predictive maintenance using real-time data and time-frequency  \nanalysis  \nDimitrios Iason Papadopoulos  \nSupervisors: Georgios Chamilothoris, Vanessa Garcia, Saioa Etxebarria  \nVitoria-Gasteiz, May 2023  \nGRADO EN INGENIERÍA MECÁNICA  \nTRABAJO FIN DE GRADO  \nMachine learning methods for predictive maintenance using real-time data and time-frequency analysis  \nAlumno/Alumna: PAPADOPOULOS DIMITRIOS IASON Director/Directora (1): GARCIA VANESSA  \nDirector/Directora (2): ETXEBARRIA SAIOA  \nFecha: 29-05-2023  \nAcknowledgments  \nFirst of all, I would like to thank my supervisor from the mechanical engineering department of the University of West Attica, Georgios Chamilothoris, for guiding me throughout the duration of my thesis and my supervisors from the mechanical engineering department of the University of the Basque Country, Vanessa Garcia and Saioa Etxebarria for their important contribution in my effort. Moreover, I would like to thank my family and friends, for supporting me for the whole duration of my studies.  \nAbstract  \nIn this diploma thesis, different techniques of Predictive Maintenance based on Machine Learning are compared. In particular, the Remaining Useful Life of a ball bearing of the shaft of a Wind Turbine was predicted with different methods: Classification algorithms, degradation models and real time updates using a Kalman Filter. In the first half, the theory of ball bearing failure mechanisms, predictive maintenance and machine learning is analyzed. At the second half, different methods are implemented for the prediction of the remaining useful life. Last, the writer comes to a conclusion about the efficiency of each method.  \nKey words: Machine Learning, Predictive Maintenance, Remaining Useful Life, Degradation Models, Classification, Kalman Filter  \nΠερίληψη  \nΣε αυτή τη διπλωματική εργασία, γίνεται σύγκριση μεθόδων μηχανικής μάθησης για προδεικτικήσυντήρηση . Ειδικότερα, γίνεται πρόβλεψη για την εναπομένουσα ωφέλιμη ζωή ενός εδράνου,στον άξονα μιας ανεμογεννήτριας, με τις εξής μεθόδους, αλγόριθμους classification, μοντέλα degradation και συνεχής ανανέωση, με χρήση του φίλτρου Kalman. Σε πρώτη φάση, αναλύεταιη θεωρία, σχετικά με τους μηχανισμούς αστοχίας των εδράνων, την μηχανική μάθηση και τηνπροδεικτική συντήρηση . Στη συνέχεια, αυτές οι μέθοδοι, χρησιμοποιούνται για τον υπολογισμότης εναπομένουσας ωφέλιμης ζωής . Τέλος, ο συγγραφές αναλύει τα συμπεράσματά του για τηναποδοτικότητα της κάθε μεθόδου .  \nKey words: Μηχανική μάθηση, Προδεικτική συντήρηση, Εναπομένουσα ωφέλιμη ζωή, Degradation Models, Classification, Φίλτρα Kalman  \nTable of Contents  \nAbstract ........................................................................................................................................................ 4  \nΠερίληψη...................................................................................................................................................... 5  \nTable of figures ........................................................................................................................................... 8  \n1.Introduction.............................................................................................................................................. 9  \n1.1 Problem Statement .......................................................................................................................... 9  \n1.2 Objectives ......................................................................................................................................... 9  \n1.3 Bearing failure .................................................................................................................................. 9  \n2.Predictive Maintenance..","cbCaibXn5dPeWAdv","https://ap.wps.com/l/cbCaibXn5dPeWAdv","pdf",2629939,1,92,"English","en",105,"# Introduction\n## Problem Statement\n## Objectives\n## Bearing failure\n# Predictive Maintenance\n## What predictive maintenance is\n## Comparison to other maintenance approaches\n## PM applications in industry\n## Condition Monitoring\n# Machine Learning\n## Data Analysis and Feature Engineering\n## Classification algorithms","[{\"question\":\"Which predictive maintenance methods are compared in the thesis?\",\"answer\":\"The thesis compares classification algorithms, degradation models, and real-time updates using a Kalman Filter for Remaining Useful Life prediction.\"},{\"question\":\"What component is selected for Remaining Useful Life prediction?\",\"answer\":\"The Remaining Useful Life is predicted for a ball bearing on the shaft of a wind turbine.\"},{\"question\":\"How is the thesis structured from theory to implementation?\",\"answer\":\"The first part analyzes bearing failure mechanisms, predictive maintenance, and machine learning, while the second part implements prediction methods and concludes by comparing their efficiency.\"}]","Machine learning methods for predictive maintenance using real-time data and time-frequency analysis - Diploma Thesis | PDF",1785725436,232,{"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},"machine-learning-methods-for-predictive-maintenance-using-real-time-data-and-time-frequency-analysis-diploma-thesis","",{"@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/machine-learning-methods-for-predictive-maintenance-using-real-time-data-and-time-frequency-analysis-diploma-thesis/119642/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which predictive maintenance methods are compared in the thesis?","Question",{"text":75,"@type":76},"The thesis compares classification algorithms, degradation models, and real-time updates using a Kalman Filter for Remaining Useful Life prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What component is selected for Remaining Useful Life prediction?",{"text":80,"@type":76},"The Remaining Useful Life is predicted for a ball bearing on the shaft of a wind turbine.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the thesis structured from theory to implementation?",{"text":84,"@type":76},"The first part analyzes bearing failure mechanisms, predictive maintenance, and machine learning, while the second part implements prediction methods and concludes by comparing their efficiency.","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"]