[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120216-en":3,"doc-seo-120216-105":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":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":29},120216,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Applications for Reliability Engineering - A Review","Big data handling and faster data processing are enabled by parallel computing, cloud computing, and the growing range of AI techniques, which in turn drives a rapid expansion of reliability-focused applications and modeling approaches. Reliability engineering spans RAMS, PHM, and AM, targeting lifecycle value realization. As AI modeling tools multiply across research topics, selecting suitable methods becomes harder. This review surveys machine learning techniques from the angle of traditional modeling, outlining a data-science workflow for applying ML at each stage, and showing how ML complements conventional methods using literature cases.","sustainability   \nArticle  \nMachine Learning Applications for Reliability Engineering: A Review  \nMathieu Payette * and Georges Abdul-Nour   \nCitation: Payette, M.; Abdul-Nour, G. Machine Learning Applications for Reliability Engineering: A Review.  \nSustainability 2023, 15, 6270. [https://](https://)[ ](https://)[doi.org/10.3390/su15076270](doi.org/10.3390/su15076270)  \nAcademic Editor: Nita Yodo  \nReceived: 23 February 2023  \nRevised: 22 March 2023  \nAccepted: 27 March 2023  \nPublished: 6 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Industrial Engineering, University of Quebec in Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada  \n* [Correspondence: mathieu.payette@uqtr.ca](Correspondence: mathieu.payette@uqtr.ca)  \nAbstract: The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of arti􀀂cial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of arti􀀂cial intelligence (AI) modeling techniques combined with the various research topics increases the dif􀀂culty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.  \nKeywords: arti􀀂cial intelligence; engineering of asset management; machine learning; prognostic and health management; reliability  \n1. Introduction  \nFor the past few years, machine learning and arti􀀂cial intelligence have been attracting the research community's attention. More and more application cases are emerging in the manufacturing environment, especially with the advancement of the Industry 4.0 vision. The digitization of the environment through connectivity and cyber􀂖physical systems is leading to the generation of big data, which has several processing challenges. This is now referred to as the 􀂓analytics disruption􀂔 due to the fact that, in general, organizations use less than 10% of the data generated for modeling and decision support [1] . This phenomenon is not escaping the reliability domain either. New ML analysis techniques have been the subject of many publications, although traditional methods in the 􀀂eld are still widely applied. The new techniques and technologies available allow the development of new applications, but also new domains, making the selection of appropriate methods more and more complex [2] . The goal of this work is to make it easier to understand the difference between traditional and ML modeling techniques, as well as how they can be applied in reliability applications. It seeks to provide a summary analysis of the different topics so that researchers interested in the application of machine learning in reliability engineering can become familiar with these different topics.  \nSection 2 de􀀂nes the different types of modeling methods. First, a de􀀂nition of mathematical modeling is presented, then the branches of statistical modeling","cbCaiv1830LoUrcJ","https://ap.wps.com/l/cbCaiv1830LoUrcJ","pdf",1894244,1,22,"English","en",105,"# Introduction\n## Modeling methods and AI history\n## Motivation and research questions\n# Data-science modeling process\n## RAMS and PHM modeling workflow\n# Systematic review of ML in reliability engineering\n## Research questions\n## Databases used and selection criteria\n## Exclusion criteria\n# Modeling techniques in RAMS and PHM","[{\"question\":\"What reliability engineering areas are considered in the review?\",\"answer\":\"The review covers reliability, availability, maintainability, and safety (RAMS), prognostics and health management (PHM), and asset management (AM), all aiming at lifecycle value.\"},{\"question\":\"What is the main objective of the publication?\",\"answer\":\"It provides an overview of machine learning techniques from the perspective of traditional modeling approaches and explains how ML can be applied throughout a data-science workflow.\"},{\"question\":\"How does the systematic review structure its analysis?\",\"answer\":\"It defines research questions about whether the work targets RAMS or PHM, which ML methods are used, what system is studied, and whether data are real, simulated, or benchmarked, then applies database- and selection-based screening.\"}]","Machine Learning Applications for Reliability Engineering - A Review | PDF",1785728769,55,{"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-applications-for-reliability-engineering-a-review","",{"@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-applications-for-reliability-engineering-a-review/120216/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What reliability engineering areas are considered in the review?","Question",{"text":75,"@type":76},"The review covers reliability, availability, maintainability, and safety (RAMS), prognostics and health management (PHM), and asset management (AM), all aiming at lifecycle value.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main objective of the publication?",{"text":80,"@type":76},"It provides an overview of machine learning techniques from the perspective of traditional modeling approaches and explains how ML can be applied throughout a data-science workflow.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the systematic review structure its analysis?",{"text":84,"@type":76},"It defines research questions about whether the work targets RAMS or PHM, which ML methods are used, what system is studied, and whether data are real, simulated, or benchmarked, then applies database- and selection-based screening.","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"]