[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119632-en":3,"doc-seo-119632-105":29,"detail-sidebar-cat-0-en-105":81},{"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":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":11},119632,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing - Conference Paper","Manufacturing operations increasingly rely on machine learning to strengthen production performance, with condition monitoring and fault detection serving as a central use case. This paper reviews how ML methods are applied in manufacturing environments to support product quality, reduce downtime, and improve operational efficiency. It emphasizes the role of data preprocessing, feature engineering, and model selection for building dependable monitoring systems. The discussion covers case studies, deployment challenges, and future trends such as edge computing, digital twins, and advanced analytics for real-time predictive and prescriptive maintenance. ","Brought to you by INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA  \nBack  \nMachine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing  \nICETAS 2024-9th IEEE International Conference on Engineering Technologies and Applied Sciences • Conference Paper • 2024 • DOI: 10.1109/ICETAS62372.2024.11120241  Mahmoud, Amena a  ; Talpur, Kazim Raza b  ; Shah, Asadullah c  ; Saini, Shilpa d  ; Juneja, Sapna e  ; +1 author  \na Kafrelsheikh University, Faculty of Computers and Information, Kafrelsheikh, Egypt  \nShow all information  \nView PDF Full text  Export   Save to list  \nDocument Impact Cited by (0) References (19) Similar documents  \nAbstract  \nThe manufacturing industry has witnessed a surge in the adoption of machine learning (ML) techniques to enhance various aspects of production processes. One critical application of ML in manufacturing is condition monitoring and fault detection, which play a pivotal role in ensuring product quality, minimizing downtime, and maximizing operational efficiency. This paper presents a comprehensive review of the use of machine learning for condition monitoring and fault detection in manufacturing environments. It also discusses the importance of data preprocessing, feature engineering, and model selection in developing robust and reliable ML-based condition monitoring systems. Furthermore, the paper addresses the case studies, challenges and future trends associated with deploying ML-driven condition monitoring, such as data quality, model interpretability, and integration with existing manufacturing systems. It also highlights emerging trends and future  \nresearch directions in this domain, including the integration of edge computing, digital twins, and advanced analytics for real-time, predictive, and prescriptive maintenance strategies. © 2024 IEEE.  \nAuthor keywords  \nCondition Monitoring; Fault Detection; Machine Learning; Sensor-based Monitoring; Supervised Learning  \nIndexed keywords  \nEngineering controlled terms  \nEngineering education; Learning systems; Maintenance; Predictive analytics; Supervised learning  \nEngineering uncontrolled terms  \nCondition; Critical applications; Faults detection; Machine learning techniques; Machinelearning; Manufacturing industries; Manufacturing IS; Production process; Products quality; Sensor-based monitoring  \nEngineering main heading  \nCondition monitoring  \n© Copyright 2025 Elsevier B.V., All rights reserved.  \n Abstract  \nAuthor keywords Indexed keywords  \nAbout Scopus  \nWhat is Scopus Content coverage Scopus blog  \nScopus API Privacy matters  \nLanguage  \n⽇本語版を表⽰する查看简体中文版本  \n查看繁體中文版本  \nПросмотр версии на русском языке  \nCustomer Service  \nHelp Tutorials Contact us  \nTerms and conditions  Privacy policy  Cookies settings  \nAll content on this site: Copyright © 2025 Elsevier B.V. , its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.  \nWe use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies  .","cbCaigArCRMEOrjg","https://ap.wps.com/l/cbCaigArCRMEOrjg","pdf",191837,1,3,"English","en",105,"# Abstract\n## Data preprocessing and feature engineering\n## Model selection and reliability\n## Challenges and future trends","[{\"question\":\"What future trends are discussed for deploying ML-driven monitoring in manufacturing?\",\"answer\":\"The paper points to integration with edge computing, digital twins, and advanced analytics to enable real-time predictive and prescriptive maintenance strategies.\"}]","Machine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing - Conference Paper | PDF",1785725390,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":76,"head_meta":78,"extra_data":80,"updated_unix":28},"machine-learning-driven-condition-monitoring-and-fault-detection-in-manufacturing-conference-paper","",{"@graph":35,"@context":75},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-driven-condition-monitoring-and-fault-detection-in-manufacturing-conference-paper/119632/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":20},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69],{"name":70,"@type":71,"acceptedAnswer":72},"What future trends are discussed for deploying ML-driven monitoring in manufacturing?","Question",{"text":73,"@type":74},"The paper points to integration with edge computing, digital twins, and advanced analytics to enable real-time predictive and prescriptive maintenance strategies.","Answer","https://schema.org",{"og:url":50,"og:type":77,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":79,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":82},[83,87,91,95,100,105,110,113,118,121,125],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":111,"slug":112},30,"research-report",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},9,"Religion & Spirituality",20,"religion-spirituality",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":116,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":96,"slug":128},19,"General","general"]