[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290856-105":59,"doc-detail-290856-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","exploring-machine-learning-algorithms-for-predictive-maintenance-in-manufacturing-industries-msc-research-project","Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries - MSc Research Project","","Investigating the potential advantages of predictive maintenance using Machine Learning (ML) algorithms for industrial process efficiency and reliability is the main objective of this study. The research evaluates feature engineering and selection strategies by comparing multiple ML algorithms, with a focus on how model unpredictability influences business choices. Multi-Layer Perceptrons achieved up to 99.4% accuracy for failure prediction, with Random Forests as a leading alternative. The study highlights data-quality constraints and limitations in model interpretability, recommending real-time data integration and advanced hybrid models for future work.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-machine-learning-algorithms-for-predictive-maintenance-in-manufacturing-industries-msc-research-project/290856/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-machine-learning-algorithms-for-predictive-maintenance-in-manufacturing-industries-msc-research-project/290856.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the study?","Question",{"text":112,"@type":113},"The study aims to investigate how ML algorithms can improve predictive maintenance for industrial efficiency and reliability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which ML algorithm achieved the highest failure-prediction accuracy?",{"text":117,"@type":113},"Multi-Layer Perceptrons (MLP) achieved up to 99.4% accuracy for failure prediction.",{"name":119,"@type":110,"acceptedAnswer":120},"What limitations did the research identify?",{"text":121,"@type":113},"The study recognized limitations related to data quality and the interpretability of the predictive models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290856,1789642824,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":26,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries  \nMSc Research Project  \nData Analytics  \nSuprith Shiva Boraiah  \nStudent ID: x22166785  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Arghir Nicolae Moldovan  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Suprith Shiva Boraiah  \nStudent ID: X22166785  \nProgramme: Data Analytics Year: 2023  \nModule: MSc Research Project  \nSupervisor: Arghir Nicolae Moldovan  \nSubmission Due  \nDate: 14-12-2023  \nProject Title: Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries  \nWord Count: 6386 Page Count: 22  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Suprith Shiva Boraiah  \nDate: 14-12-2023  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nExploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries  \nSuprith Shiva Boraiah  \nx22166785  \nAbstract  \nInvestigating the potential advantages of predictive maintenance using Machine Learning (ML) algorithms for the industrial sector's process efficiency and reliability is the main objective of this study. The purpose of this study was to evaluate feature engineering and selection strategies for improving model performance by comparing several ML algorithms. The primary goal of the research was to find out how the unpredictability of predictive maintenance models affects business choices. It was discovered that the most effective algorithms for failure prediction are Multi-Layer Perceptrons (MLP), which achieved an accuracy of up to 99.4 percent. The other top technique is Random Forests (RF). Data preparation is essential for improving algorithmic predictions, as the study also showed. Nevertheless, limitations related to data quality and the intelligibility of the models were recognized. To enhance maintenance systems'predictive capabilities in production settings, future studies should integrate data in realtime and apply advanced hybrid models.  \nKeywords: Predictive Maintenance, Machine Learning, MLP, Random Forest Classifier, Feature Engineering, Hybrid Modelling, Support Vector Machine.  \n1 Introduction  \n1.1 Background  \nIn the era of manufacturing, the effectiveness & efficiency of machinery plays a very important role in explaining the overall productivity and economic possibilities of operations. The construction sector is vulnerable to countless challenges restricting unexpected equipment breakdowns, its results are production delays, magnified maintenance costs, and subpar quality of goods. In highlighting these challenges, predictive maintenance emerges as a crucial approach to expect the terrible failure of machines.  \nPredictive maintenance exceeds traditional maintenance procedu","cbCain4BVkFNNjy0","https://ap.wps.com/l/cbCain4BVkFNNjy0","pdf",1090549,24,"English","# Introduction\n## Background\n## Problem Statement\n## Aim\n## Objectives\n## Research Questions","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to investigate how ML algorithms can improve predictive maintenance for industrial efficiency and reliability.\"},{\"question\":\"Which ML algorithm achieved the highest failure-prediction accuracy?\",\"answer\":\"Multi-Layer Perceptrons (MLP) achieved up to 99.4% accuracy for failure prediction.\"},{\"question\":\"What limitations did the research identify?\",\"answer\":\"The study recognized limitations related to data quality and the interpretability of the predictive models.\"}]","Exploring Machine Learning Algorithms for Predictive Maintenance in Manufacturing Industries - MSc Research Project | PDF"]