[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118440-en":3,"doc-seo-118440-105":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},118440,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","PREDICTION OF INDUCTION MOTOR FAULTS USING MACHINE LEARNING - Dissertation","Induction motor failures reduce reliability and drive costly downtime, motivating predictive fault detection using machine learning. The dissertation investigates induction motor fault types, reviews traditional and maintenance approaches, and establishes an ML-based framework for fault prediction and diagnosis. It details data collection and preprocessing, including tagging, cleaning, transformation, feature selection, and data splitting, followed by model training and evaluation using metrics such as accuracy, precision, recall, F1-score, and the confusion matrix. The work aims to identify faults early, support predictive maintenance decisions, and reduce maintenance-related risk while improving diagnostic performance.","PREDICTION OF INDUCTION MOTOR FAULTS USING MACHINE  \nLEARNING  \nANYIM, JUSTUS TOCHUKWU  \n(21PCK02297)  \nB.Eng, Electrical Electronics Engineering, Bells University of Technology, Ota  \nJULY, 2023  \nPREDICTION OF INDUCTION MOTOR FAULTS USING MACHINE  \nLEARNING  \nBY  \nANYIM, JUSTUS TOCHUKWU  \n(21PCK02297)  \nB.Eng, Electrical Electronics Engineering, Bells University of Technology, Ota  \nA DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF ENGINEERING DEGREE (M.Eng.) IN ELECTRICAL AND ELECTRONICS ENGINEERING IN THE DEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEERING, COLLEGE OF ENGINEERING, COVENANT UNIVERSITY, OTA, OGUN STATE  \nJULY, 2023  \nACCEPTANCE  \nThis is to attest that this dissertation is accepted in partial fulfillment of the requirements for the award of the degree of Master of Engineering in Electrical and Electronics Engineering, Department of Electrical and Information Engineering, College of Engineering, Covenant University Ota Nigeria.  \nMs Adefunke F. Oyinloye  \n(Secretary, School of Postgraduate Studies) Signature and Date  \nProf. Akan B. Williams  \n(Dean, School of Postgraduate Studies) Signature and Date  \nDECLARATION  \nI, ANYIM, JUSTUS TOCHUKWU (21PCK02297), declare that this research was carried out by me under the supervision of Dr. Ademola Abdulkareem of the Department of Electrical and Information Engineering, College of Engineering, Covenant University, Ota, Nigeria. I attest that the dissertation has not been presented either wholly or partially for the award of any degree elsewhere. All sources of data and scholarly information used in this dissertation are duly acknowledged.  \nANYIM, JUSTUS TOCHUKWU  \nSignature and Date  \nCERTIFICATION  \nWe certify that this dissertation titled “PREDICTION OF INDUCTION MOTOR FAULTS USING MACHINE LEARNING” is an original research work carried out by ANYIM, JUSTUS TOCHUKWU (21PCK02297), in the Department of Electrical and Information Engineering, College of Engineering, Covenant University, Ota, Ogun State, Nigeria under the supervision of Dr. Ademola Abdulkareem. We have examined and found this work acceptable as part of the requirements for the award of Master of Electrical and Electronics Engineering.  \nDr. Ademola Abdulkareem  \n(Supervisor) Signature and Date  \nDr. Isaac A. Samuel  \n(Head of Department) Signature and Date  \nDr. Oluwafemi M. Onibonoje  \n(External Examiner) Signature and Date  \nProf. Akan B. Williams  \n(Dean, School of Postgraduate Studies) Signature and Date  \nDEDICATION  \nThis dissertation is dedicated, first and foremost, to the Almighty God for His mercies, grace, wisdom, and grace throughout the Masters’ program. It is especially dedicated to my parents Chief and Mrs. Stanley Anyim, and my lovely siblings Dozie, Excel, Ijeoma, Ifunanya, Ikechukwu and Kelechi.  \nACKNOWLEDGMENTS  \nFirstly, I want to thank God for His constant guidance, blessings, and strength throughout this research. I am truly grateful for His grace upon my life.  \nMy sincere gratitude also goes out to my family for their unending support, inspiration, and tolerance. Their unwavering belief in me and continuous support have been instrumental in my success. Their sacrifices during the demanding phases have been invaluable, and I will always be appreciative of their constant support.  \nI would like to express my sincere gratitude to my supervisor, Dr. Ademola Abdulkareem for his exceptional guidance, invaluable advice, extensive knowledge, and insightful contributions throughout this project. His expertise and unwavering support have been instrumental in the successful completion of this research.  \nFinally, I would like to thank my colleagues and friends who have contributed worthwhile ideas, suggestions, and support during this research. Their collaborative spirit and willingness to share their knowledge and experiences have been invaluable in enriching the overall quality of this work.  \nTABLE OF CO","cbCaipuU7nfEpQWH","https://ap.wps.com/l/cbCaipuU7nfEpQWH","pdf",115215,1,14,"English","en",105,"# Introduction\n## Background to the study\n## Statement of the Problem\n## Aim and Objectives\n## Scope of the Study\n## Justification of the Study\n## Limitation of Research\n# Literature Review\n## Overview of induction motors and faults\n## Traditional methods for fault identification and diagnosis\n## Evolution of induction motor maintenance\n## Machine Learning approach for predictive maintenance\n## Evaluation metrics in machine learning\n## Data collection and processing techniques\n## Summary of gaps identified\n# Materials and Methodology\n## Materials used\n## Data collection and preprocessing\n## Model training","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses the challenge of identifying and diagnosing induction motor faults early to support predictive maintenance and reduce downtime.\"},{\"question\":\"How does the study approach machine learning for fault prediction?\",\"answer\":\"It reviews induction motor faults and maintenance methods, then describes an ML workflow including data collection, preprocessing, feature selection, model training, and evaluation.\"},{\"question\":\"Which evaluation metrics are used to assess the ML models?\",\"answer\":\"The dissertation uses accuracy, precision, recall, F1-score, and the confusion matrix as evaluation metrics.\"}]","PREDICTION OF INDUCTION MOTOR FAULTS USING MACHINE LEARNING - 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