{"title":"数字孪生驱动的核电设备智能维修决策系统及关键赋能技术","authors":"qingfeng xu, Guanghui Zhou, Chao Zhang, Fengtian Chang, Qian Huang, M. Zhang, Yifan Zhi","doi":"10.12688/digitaltwin.17695.1","DOIUrl":null,"url":null,"abstract":"In the life cycle of nuclear power equipment (NPE), the long-term high-safety maintenance services play a vital role in ensuring their optimal operation. However, as the complex system equipment with high-safety requirements and high costs, there are lots of limitations of traditional time-based maintenance strategies for NPE. For example, the maintenance service process is invisible, the condition monitoring is mainly based on manual inspection, and the maintenance decision-making mainly depends on personal experience passively. Digital twins (DT) are an effective way to break the “information isolated island” in the whole life cycle, which can give full play to the value of data to realize the visualization of operation process of NPE. Nevertheless, nowadays, the application of DT in the field of nuclear industry is at the exploration stage, and there is lacking systematic and practical research. Thus, a novel DT-driven intelligent maintenance decision-making system involving three key-enabling technologies is proposed in this paper. Firstly, the DT-driven maintenance service mode is introduced, and its corresponding system framework is built. Then, the key enabling technologies such as DT modeling, condition monitoring and dynamic pre-alarm, and systematic intelligent maintenance decision-making and verification are expounded in detail. Finally, the cooling water pump is regarded as the case to verify the proposed method. The DT prototype system is developed and verified in the novel system, which demonstrates the novel system and the three key-enabling technologies are feasible and practical.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Digital twin-driven intelligent maintenance decision-making system and key-enabling technologies for nuclear power equipment\",\"authors\":\"qingfeng xu, Guanghui Zhou, Chao Zhang, Fengtian Chang, Qian Huang, M. Zhang, Yifan Zhi\",\"doi\":\"10.12688/digitaltwin.17695.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the life cycle of nuclear power equipment (NPE), the long-term high-safety maintenance services play a vital role in ensuring their optimal operation. However, as the complex system equipment with high-safety requirements and high costs, there are lots of limitations of traditional time-based maintenance strategies for NPE. For example, the maintenance service process is invisible, the condition monitoring is mainly based on manual inspection, and the maintenance decision-making mainly depends on personal experience passively. Digital twins (DT) are an effective way to break the “information isolated island” in the whole life cycle, which can give full play to the value of data to realize the visualization of operation process of NPE. Nevertheless, nowadays, the application of DT in the field of nuclear industry is at the exploration stage, and there is lacking systematic and practical research. Thus, a novel DT-driven intelligent maintenance decision-making system involving three key-enabling technologies is proposed in this paper. Firstly, the DT-driven maintenance service mode is introduced, and its corresponding system framework is built. Then, the key enabling technologies such as DT modeling, condition monitoring and dynamic pre-alarm, and systematic intelligent maintenance decision-making and verification are expounded in detail. Finally, the cooling water pump is regarded as the case to verify the proposed method. The DT prototype system is developed and verified in the novel system, which demonstrates the novel system and the three key-enabling technologies are feasible and practical.\",\"PeriodicalId\":29831,\"journal\":{\"name\":\"Digital Twin\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Twin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12688/digitaltwin.17695.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Twin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/digitaltwin.17695.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Digital twin-driven intelligent maintenance decision-making system and key-enabling technologies for nuclear power equipment
In the life cycle of nuclear power equipment (NPE), the long-term high-safety maintenance services play a vital role in ensuring their optimal operation. However, as the complex system equipment with high-safety requirements and high costs, there are lots of limitations of traditional time-based maintenance strategies for NPE. For example, the maintenance service process is invisible, the condition monitoring is mainly based on manual inspection, and the maintenance decision-making mainly depends on personal experience passively. Digital twins (DT) are an effective way to break the “information isolated island” in the whole life cycle, which can give full play to the value of data to realize the visualization of operation process of NPE. Nevertheless, nowadays, the application of DT in the field of nuclear industry is at the exploration stage, and there is lacking systematic and practical research. Thus, a novel DT-driven intelligent maintenance decision-making system involving three key-enabling technologies is proposed in this paper. Firstly, the DT-driven maintenance service mode is introduced, and its corresponding system framework is built. Then, the key enabling technologies such as DT modeling, condition monitoring and dynamic pre-alarm, and systematic intelligent maintenance decision-making and verification are expounded in detail. Finally, the cooling water pump is regarded as the case to verify the proposed method. The DT prototype system is developed and verified in the novel system, which demonstrates the novel system and the three key-enabling technologies are feasible and practical.
期刊介绍:
Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.
The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches.
The scope of Digital Twin includes, but is not limited to, the following areas:
● Digital twin concepts, architecture, and frameworks
● Digital twin theory and method
● Digital twin key technologies and tools
● Digital twin applications and case studies
● Digital twin implementation
● Digital twin services
● Digital twin security
● Digital twin standards
Digital twin also focuses on applications within and across broad sectors including:
● Smart manufacturing
● Aviation and aerospace
● Smart cities and construction
● Healthcare and medicine
● Robotics
● Shipping, vehicles and railways
● Industrial engineering and engineering management
● Agriculture
● Mining
● Power, energy and environment
Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.