[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4075":3,"related-4075":71},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":47,"slug":48,"view_count":49,"doi":50,"paper":51,"created_at":70},4075,"Development of an Integrated Digital Twin Framework for Online Monitoring and Anomaly Detection","https:\u002F\u002Fdoi.org\u002F10.36001\u002Fijphm.2026.v17i2.5076","Digital Twins (DTs) are emerging as an important enabling technology in the nuclear field, as their development leverages the ongoing digitalization of nuclear systems and offers many advantages for optimizing nuclear operations. Nuclear DTs can be used for predictive maintenance, enhanced operations, improved scheduling, cradle-to-grave lifecycle management, decision support, and more. While the advantages are clear, continued DT development is needed, as many gaps remain, including technological and regulatory standardization, validation and verification, and digital-to-physical communication. To help close these gaps, a forced-water-flow experimental system at the University of Tennessee, Knoxville (UTK) was used to develop a complete, integrated DT, offering an end-to-end use case in the nuclear sector and related disciplines. For this work, a Long Short-Term Memory (LSTM) network was used for forecasting, and an interquartile range (IQR) LSTM hybrid method was used for anomaly detection. This was all interfaced with LabVIEW, enabling real-time data analysis and forecasting. A GUI was then created to provide the operator with a clear indication when a system anomaly is detected, enabling better insights during operation. The resulting platform demonstrates an end-to-end proof of concept for integrating data acquisition, machine learning-based forecasting and anomaly detection, and operator interaction within an experimental DT for various nuclear and related engineering systems.","数字孪生（Digital Twins, DTs）正逐渐成为核领域的一项重要赋能技术，其发展得益于核系统持续的数字化进程，并为优化核操作提供了诸多优势。核数字孪生可用于预测性维护、增强操作、改进调度、全生命周期管理、决策支持等。尽管优势明显，但仍需持续推进数字孪生的发展，因为尚存许多差距，包括技术与监管标准化、验证与确认，以及数字到物理的通信。为帮助弥合这些差距，田纳西大学诺克斯维尔分校（UTK）的一个强迫水流量实验系统被用于开发一个完整集成的数字孪生，为核领域及相关学科提供了一个端到端的使用案例。在这项工作中，采用长短期记忆（LSTM）网络进行预测，并采用四分位距（IQR）LSTM混合方法进行异常检测。所有这些都与LabVIEW接口，实现了实时数据分析和预测。随后创建了一个图形用户界面（GUI），当检测到系统异常时，能向操作员提供清晰的指示，从而在操作过程中获得更好的洞察。由此产生的平台展示了一个端到端的原理验证，即在实验性数字孪生中集成了数据采集、基于机器学习的预测与异常检测以及操作员交互，适用于各种核及相关工程系统。",null,"International Journal of Prognostics and Health Management","2026-09-30T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦核领域数字孪生与异常检测，与三农、农业信息化、智慧农业等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":46},[28,37],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":36},"I1",11,true,[33],"digital twin",[35],"数字孪生",[],{"code":38,"score":39,"supported":31,"title":40,"abstract":41,"tags":45},"I2",9,[],[42,43,44],"机器学习","machine learning","decision support",[],"农业相关性需要复核",[],"DevelopmentofanIntegratedDigitalTwinFram-4075",2,"10.36001\u002Fijphm.2026.v17i2.5076",{"doi":50,"openalex_id":52,"authors":53,"venue":10,"cited_by_count":15,"oa_url":61,"card":62,"direction":68,"ingested_from":69},"W7215131850",[54,57,59],{"name":55,"orcid":56},"Lauren Bailey","https:\u002F\u002Forcid.org\u002F0009-0001-2109-9295",{"name":58,"orcid":9},"Jamie Coble",{"name":60,"orcid":9},"Xingang Zhao","https:\u002F\u002Fpapers.phmsociety.org\u002Findex.php\u002Fijphm\u002Farticle\u002Fdownload\u002F5076\u002F3028",{"tldr":63,"method":64,"finding":65,"direction":66,"opportunity":67},"构建集成数字孪生框架，用LSTM与IQR-LSTM实现核系统在线监测与异常检测。","LSTM预测与IQR-LSTM混合异常检测，结合LabVIEW实时数据与GUI。","平台实现数据采集、机器学习预测与操作员交互的端到端数字孪生概念验证。","其他","可将该数字孪生框架迁移至农业装备或温室环境监测，探索跨领域异常检测与决策支持。","智慧农业 \u002F 农业物联网","openalex","2026-10-03T23:30:18.790020Z",{"total":15,"page":72,"page_size":72,"items":73},1,[]]