[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119973-en":3,"doc-seo-119973-105":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},119973,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes - A Review","Light-emitting diodes (LEDs) have transformed lighting through versatility, higher efficiency, longer lifetime, and improved reliability. Growing needs for extended service life and dependable operation have driven extensive LED prognostics and lifetime-estimation research, from traditional failure-data analysis to degradation modeling and machine-learning approaches. Yet, few reviews comprehensively systematize evolving ML methods for fault detection, diagnostics, and lifetime prediction. This review surveys ML-driven diagnostic and prognostic techniques within a PHM taxonomy, highlighting supervision requirements, target-problem types, core algorithm trade-offs, and future digital-twin opportunities.","This is the peer reviewed version of the following article: Ibrahim, M. S. , Fan, J. , Yung, W. K. C. , Prisacaru, A. , van Driel, W. , Fan, X. , & Zhang, G. (2020) . Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes. Laser and Photonics Reviews, 14(12), 2000254 which has been published in final form at [https://doi.org/10.1002/lpor.202000254. This article may be](https://doi.org/10.1002/lpor.202000254. This article may be) used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.  \nMachine Learning and Digital Twin Driven Diagnostics and Prognostics ofLight-emitting Diodes: A Review  \nMesfin Seid Ibrahim1,2*, Jiajie Fan3,4,5*, Winco K.C. Yung1, Alexandru Prisacaru5,6, Willem van Driel5,7,  \nXuejun Fan8 and Guoqi Zhang5  \n1Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 2College of Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha 208, Ethiopia 3Academy for Engineering and Technology, Fudan University, Shanghai 200433, China 4College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China 5EEMCS Faculty, Delft University of Technology, Delft 2628, Netherlands  \n6Robert Bosch GmbH, Reutlingen 72703, Germany  \n7Signify, Eindhoven 5656 AE, Netherlands  \n8Department of Mechanical Engineering, Lamar University, Beaumont, Texas 77710, USA  \n*[Corresponding authors: Dr. Jiajie Fan ](Corresponding authors: Dr. Jiajie Fan jay.fan@connect.polyu.hk)[jay.fan@connect.polyu.hk](Corresponding authors: Dr. Jiajie Fan jay.fan@connect.polyu.hk); [Mesfin Seid ](Mesfin Seid mesfin.ibrahim@connect.polyu.hk)[mesfin.ibrahim@connect.polyu.hk](Mesfin Seid mesfin.ibrahim@connect.polyu.hk)  \nAbstract  \nLight-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation analysis and machine learning based approaches over the past couple of years. However, there have been few reviews that systematically address the currently evolving machine learning (ML) algorithms and methods for fault detection, diagnostics and lifetime prediction of LEDs. To address those deficiencies, we provide a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability and lifetime assessment of LEDs. After a brief description of the overall taxonomy of prognostics and health management (PHM) methods and algorithms, particular attention is given to the machine learning based algorithms characterized by their need for human supervision and type of target prognostic problems.  \nThe fundamental principles, and the pros and cons of methods including artificial neural networks, K-nearest neighbors, Principal component analysis, hidden Markov models, support vector machines, relevance vector machines and Bayesian networks are emphasized. Finally, we provide discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability a","cbCaiodlc6iU37q9","https://ap.wps.com/l/cbCaiodlc6iU37q9","pdf",3231425,1,54,"English","en",105,"# Introduction\n## LED evolution and motivation for PHM\n## LED prognostics and lifetime estimation background\n# Machine Learning and Digital Twin Framework\n## PHM taxonomy and method overview\n## Supervision needs and target prognostic problem types\n# Core Algorithms and Trade-offs\n## Neural networks and K-nearest neighbors\n## PCA, hidden Markov models, and SVMs\n## Relevance vector machines and Bayesian networks\n# Future Directions\n## Digital twin from package to system level\n## Challenges and opportunities","[{\"question\":\"Why are LEDs a focus of diagnostics and prognostics research?\",\"answer\":\"LEDs offer long lifetime and high reliability, but increasing lifetime and maintaining dependable performance require accurate fault detection and lifetime prediction methods.\"},{\"question\":\"What does the review cover regarding machine learning for LED health management?\",\"answer\":\"It surveys ML-based diagnostic and prognostic algorithms for LEDs, organizing approaches within a PHM taxonomy and emphasizing supervision requirements and prognostic target-problem types.\"},{\"question\":\"Which ML methods are highlighted as fundamental to LED diagnostics and prognostics?\",\"answer\":\"The review emphasizes methods including artificial neural networks, K-nearest neighbors, PCA, hidden Markov models, support vector machines, relevance vector machines, and Bayesian networks, along with their pros and cons.\"}]","Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes - A Review | PDF",1785727370,136,{"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-and-digital-twin-driven-diagnostics-and-prognostics-of-light-emitting-diodes-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-and-digital-twin-driven-diagnostics-and-prognostics-of-light-emitting-diodes-a-review/119973/",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},"Why are LEDs a focus of diagnostics and prognostics research?","Question",{"text":75,"@type":76},"LEDs offer long lifetime and high reliability, but increasing lifetime and maintaining dependable performance require accurate fault detection and lifetime prediction methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the review cover regarding machine learning for LED health management?",{"text":80,"@type":76},"It surveys ML-based diagnostic and prognostic algorithms for LEDs, organizing approaches within a PHM taxonomy and emphasizing supervision requirements and prognostic target-problem types.",{"name":82,"@type":73,"acceptedAnswer":83},"Which ML methods are highlighted as fundamental to LED diagnostics and prognostics?",{"text":84,"@type":76},"The review emphasizes methods including artificial neural networks, K-nearest neighbors, PCA, hidden Markov models, support vector machines, relevance vector machines, and Bayesian networks, along with their pros and cons.","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"]