[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128117-en":3,"doc-seo-128117-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128117,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Implementation of Machine Learning to Predict Cable Failures in Electrical Networks - Thesis","Electrical networks are critical infrastructure, and cable failures can cause outages, financial loss, and reputational damage. Traditional inspection and reactive repair methods often fail to prevent unexpected failures driven by aging, environmental conditions, mechanical stress, and load imbalance. The research develops a machine learning predictive model for Dubai Electricity and Water Authority (DEWA) by using historical failure records, environmental variables, operational parameters, and sensor readings to learn degradation patterns.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \nSpring 2025  \nImplementation of Machine Learning to Predict Cable Failures in Electrical Networks  \nSultan Bader Albahri[sba7186@rit.edu](sba7186@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nAlbahri, Sultan Bader, \"Implementation of Machine Learning to Predict Cable Failures in Electrical Networks\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nImplementation of Machine Learning to Predict Cable Failures in Electrical  \nNetworks  \nby  \nSultan Bader Albahri  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Professional Studies: Data Analytics  \nDepartment of Graduate Programs & Research  \nRochester Institute of Technology  \nRIT Dubai  \nSpring 2025  \nRIT  \nMaster of Science in Professional Studies: Data Analytics  \nGraduate Thesis Approval  \nStudent Name: Sultan Bader Albahri  \nGraduate Capstone Title: Implementation of Machine Learning to Predict Cable Failures in Electrical Networks  \nGraduate Thesis Committee:  \nName: Dr. Sanjay Modak Date:  \nChair of committee  \nName: Dr. Hammou Messatfa Date:  \nMember of committee  \nACKNOWLEDGMENT  \nI would like to express my deepest gratitude to my advisor, Dr. Hammou Messatfa, for his invaluable guidance, continuous support, and insightful feedback throughout this research. His expertise has been instrumental in shaping this thesis.  \nI also extend my sincere appreciation to Dr. Sanjay Modak, the Department Chair, for his support and encouragement during my academic journey.  \nA special thank you to RIT Dubai for providing the resources and academic environment that enabled me to conduct this research.  \nLastly, I am deeply grateful to my family and friends for their unwavering support, patience, and encouragement. Their belief in me has been my greatest source of strength throughout this journey.  \nABSTRACT  \nElectrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution.  \nThis research focuses on developing an ML-based predictive model to forecast cable failuresin Dubai Electricity and Water Authority (DEWA)’s electrical network. The study leverages historical failure data, environmental factors, operational parameters, and real-time sensor readings to identify patterns associated with cable degradation. Various data preprocessing techniques, including missing value imputation, outlier detection (using Mahalanobis distance), and feature selection, were applied to enhance data quality and model reliability. The research follows the CRISP-DM methodology, ensuring a structured approach to business understanding, data exploration, model development, and evaluation.  \nMultiple machine learning algorithms, including Neural Networks, Support Vector Machines (SVM), Logistic Regression, and Random Trees, were explored for predictive modeling. The models were validated using partitioning techniques, data balancing strategies, and evaluation metrics such as AUC-ROC curves, confusion matrices, and predictor importance analysis. Experimental results demonstrate that ML-driven predictions can sig","cbCaipzyqOpzgbna","https://ap.wps.com/l/cbCaipzyqOpzgbna","pdf",2926776,2,1,98,"English","en",105,"# Acknowledgment\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1","[{\"question\":\"What problem does this thesis address?\",\"answer\":\"It addresses unexpected electrical cable failures in power distribution networks and the limitations of traditional maintenance approaches.\"},{\"question\":\"What data and preprocessing methods are used to build the predictive models?\",\"answer\":\"The study uses historical failure data, environmental factors, operational parameters, and real-time sensor readings, applying missing value imputation, outlier detection via Mahalanobis distance, and feature selection.\"},{\"question\":\"Which machine learning models are evaluated, and how is performance measured?\",\"answer\":\"Neural Networks, Support Vector Machines, Logistic Regression, and Random Trees are explored, with validation through data partitioning and balancing and evaluation using AUC-ROC, confusion matrices, and predictor-importance analysis.\"}]","Implementation of Machine Learning to Predict Cable Failures in Electrical Networks - Thesis | PDF",1785944915,247,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"implementation-of-machine-learning-to-predict-cable-failures-in-electrical-networks-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/implementation-of-machine-learning-to-predict-cable-failures-in-electrical-networks-thesis/128117/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this thesis address?","Question",{"text":76,"@type":77},"It addresses unexpected electrical cable failures in power distribution networks and the limitations of traditional maintenance approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and preprocessing methods are used to build the predictive models?",{"text":81,"@type":77},"The study uses historical failure data, environmental factors, operational parameters, and real-time sensor readings, applying missing value imputation, outlier detection via Mahalanobis distance, and feature selection.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are evaluated, and how is performance measured?",{"text":85,"@type":77},"Neural Networks, Support Vector Machines, Logistic Regression, and Random Trees are explored, with validation through data partitioning and balancing and evaluation using AUC-ROC, confusion matrices, and predictor-importance analysis.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]