U. Amin , D. Kim , F.N. Ahmed , G. Ahmad , M.J. Hossain
{"title":"能源行业智能资产管理的数字孪生:最先进的技术","authors":"U. Amin , D. Kim , F.N. Ahmed , G. Ahmad , M.J. Hossain","doi":"10.1016/j.eswa.2025.128358","DOIUrl":null,"url":null,"abstract":"<div><div>With the most recent developments in the Internet of Things (IoT), Machine Learning, and Big Data, digital twins (DTs) are gaining popularity across several sectors. Since digital twin (DT) could be viewed as a cyber-physical system, many DT applications have been successfully implemented for the Industrial Internet of Things (IIoT). Following DT’s success with Industry 4.0, DT is increasingly receiving the attention of academia and industry for smart asset management (SAM) in Energy 4.0. Nevertheless, there has been a notable absence of research papers specifically dedicated to reviewing the applications of DTs in the context of the SAM domain within the Energy 4.0 paradigm. Hence, this paper addresses this gap by thoroughly examining the latest advancements in DT research on SAM in Energy 4.0. It comprehensively explores the fundamental aspects of DTs, provides insights into their current progress within the Energy 4.0 framework, and elucidates the various applications of DTs within the SAM domain. Furthermore, the paper highlights the existing challenges and presents potential directions for future research endeavors in this field. This review will assist industry experts in implementing DT in SAM applications and provide new research directions for researchers.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"289 ","pages":"Article 128358"},"PeriodicalIF":7.5000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital twins for smart asset management in the energy industry: State-of-the-art\",\"authors\":\"U. Amin , D. Kim , F.N. Ahmed , G. Ahmad , M.J. Hossain\",\"doi\":\"10.1016/j.eswa.2025.128358\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the most recent developments in the Internet of Things (IoT), Machine Learning, and Big Data, digital twins (DTs) are gaining popularity across several sectors. Since digital twin (DT) could be viewed as a cyber-physical system, many DT applications have been successfully implemented for the Industrial Internet of Things (IIoT). Following DT’s success with Industry 4.0, DT is increasingly receiving the attention of academia and industry for smart asset management (SAM) in Energy 4.0. Nevertheless, there has been a notable absence of research papers specifically dedicated to reviewing the applications of DTs in the context of the SAM domain within the Energy 4.0 paradigm. Hence, this paper addresses this gap by thoroughly examining the latest advancements in DT research on SAM in Energy 4.0. It comprehensively explores the fundamental aspects of DTs, provides insights into their current progress within the Energy 4.0 framework, and elucidates the various applications of DTs within the SAM domain. Furthermore, the paper highlights the existing challenges and presents potential directions for future research endeavors in this field. This review will assist industry experts in implementing DT in SAM applications and provide new research directions for researchers.</div></div>\",\"PeriodicalId\":50461,\"journal\":{\"name\":\"Expert Systems with Applications\",\"volume\":\"289 \",\"pages\":\"Article 128358\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Systems with Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0957417425019773\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425019773","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Digital twins for smart asset management in the energy industry: State-of-the-art
With the most recent developments in the Internet of Things (IoT), Machine Learning, and Big Data, digital twins (DTs) are gaining popularity across several sectors. Since digital twin (DT) could be viewed as a cyber-physical system, many DT applications have been successfully implemented for the Industrial Internet of Things (IIoT). Following DT’s success with Industry 4.0, DT is increasingly receiving the attention of academia and industry for smart asset management (SAM) in Energy 4.0. Nevertheless, there has been a notable absence of research papers specifically dedicated to reviewing the applications of DTs in the context of the SAM domain within the Energy 4.0 paradigm. Hence, this paper addresses this gap by thoroughly examining the latest advancements in DT research on SAM in Energy 4.0. It comprehensively explores the fundamental aspects of DTs, provides insights into their current progress within the Energy 4.0 framework, and elucidates the various applications of DTs within the SAM domain. Furthermore, the paper highlights the existing challenges and presents potential directions for future research endeavors in this field. This review will assist industry experts in implementing DT in SAM applications and provide new research directions for researchers.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.