State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important ...State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.展开更多
This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to ...This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate n展开更多
电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平...电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平台对各阶段研究成果进行集成和性能提升。数字孪生(digital twin,DT)技术加强了对变压器多物理部件运行参数的监测和集成,通过在虚拟空间多物理多尺度建模实现对变压器综合故障分析,是变压器PHM演变的重要方向。鉴于此,对面向电力变压器PHM的DT技术进行了研究,阐述了变压器PHM内涵,归纳了面向变压器PHM的DT技术框架、关键技术、面临的挑战及未来发展趋势等,分析了DT与支撑其的数据、模型、计算等之间的关系,旨在为变压器运维领域数字孪生技术研究人员提供参考。展开更多
The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cann...The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.展开更多
Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring ...Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.展开更多
数字孪生(Digital twin,DT)技术与预测和健康管理(Prognostics and health management,PHM)技术是智能制造领域中的两个热点研究方向。在对PHM技术现状总结分析的基础上,归纳当前制约PHM技术发展和应用的关键性问题如下:设备故障机理研...数字孪生(Digital twin,DT)技术与预测和健康管理(Prognostics and health management,PHM)技术是智能制造领域中的两个热点研究方向。在对PHM技术现状总结分析的基础上,归纳当前制约PHM技术发展和应用的关键性问题如下:设备故障机理研究不透彻、全生命周期数据不完备、健康状态监测方法不足、多层级状态信息综合不足以及不确定性管理问题。并阐述数字孪生技术在解决这些问题过程中的独特优势,提出将基于第一性原理的多维数字孪生模型构建、虚实空间的多维数据映射、孪生体技术状态一致性度量与模型的高效迭代修正以及基于多域特征的系统健康评估、预测与维护决策作为关键技术构建DT-PHM研究架构。随着技术不断推进与发展,两项技术深度融合,基于数字孪生的复杂系统健康管理技术必将成为未来装备全生命周期视情维修和预测性维修的关键技术之一。展开更多
As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing ...As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.展开更多
Maintenance of aero-engine fleets is crucial for the efficiency,safety,and reliability of the aviation industry.With the increasing demand for air transportation,maintaining high-performing aero-engines has become sig...Maintenance of aero-engine fleets is crucial for the efficiency,safety,and reliability of the aviation industry.With the increasing demand for air transportation,maintaining high-performing aero-engines has become significant.Collaborative maintenance,specifically targeting aero-engine fleets,involves the coordination of multiple tasks and resources to enhance management efficiency and reduce costs.Digital Twin(DT)technology provides essential technical support for the intelligent operation and maintenance of aero-engine fleets.DT maps physical object properties to the virtual world,creating high-fidelity,dynamic models.However,DT-enhanced collaborative maintenance faces various challenges,including the construction of complex system-layer DT models,management of massive integrated DT data,and the development of fusion mechanisms and decision-making methods for DT data and models.Overcoming these challenges will allow the aviation industry to optimize aero-engine fleet maintenance,ensuring safety,efficiency,and cost-effectiveness while meeting the growing demand for air transportation.展开更多
The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casti...The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.展开更多
The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional di...The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.展开更多
基金This work is financially supported by the National Key Research and Development Program of China(2016YFB1101700)the National Natural Science Foundation of China(51875030)the Academic Excellence Foundation of BUAA for PhD Students.
文摘State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.
文摘This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate n
文摘电力变压器故障预测和健康管理(prognostics and health management,PHM)对于实现其从传统定期维修转向视情维修和预测维修进而保障设备的健康运行具有重要意义。长期以来变压器PHM技术一直停留在理论研究阶段,缺乏有效的技术体系和平台对各阶段研究成果进行集成和性能提升。数字孪生(digital twin,DT)技术加强了对变压器多物理部件运行参数的监测和集成,通过在虚拟空间多物理多尺度建模实现对变压器综合故障分析,是变压器PHM演变的重要方向。鉴于此,对面向电力变压器PHM的DT技术进行了研究,阐述了变压器PHM内涵,归纳了面向变压器PHM的DT技术框架、关键技术、面临的挑战及未来发展趋势等,分析了DT与支撑其的数据、模型、计算等之间的关系,旨在为变压器运维领域数字孪生技术研究人员提供参考。
基金supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010 (5400-202199534A-05-ZN)。
文摘The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.
基金co-supported by the National Natural Science Foundation of China(Nos.U223321251875014)+1 种基金the Beijing Natural Science Foundation,China(No.L221008)the China Scholarship Council(No.202106020001).
文摘Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
文摘数字孪生(Digital twin,DT)技术与预测和健康管理(Prognostics and health management,PHM)技术是智能制造领域中的两个热点研究方向。在对PHM技术现状总结分析的基础上,归纳当前制约PHM技术发展和应用的关键性问题如下:设备故障机理研究不透彻、全生命周期数据不完备、健康状态监测方法不足、多层级状态信息综合不足以及不确定性管理问题。并阐述数字孪生技术在解决这些问题过程中的独特优势,提出将基于第一性原理的多维数字孪生模型构建、虚实空间的多维数据映射、孪生体技术状态一致性度量与模型的高效迭代修正以及基于多域特征的系统健康评估、预测与维护决策作为关键技术构建DT-PHM研究架构。随着技术不断推进与发展,两项技术深度融合,基于数字孪生的复杂系统健康管理技术必将成为未来装备全生命周期视情维修和预测性维修的关键技术之一。
基金supported by China National Heavy Duty Truck Group Co.,Ltd.(Grant No.YF03221048P)the Shanghai Municipal Bureau of Market Supervision and Administration(Grant No.2022-35)New Young TeachersResearch Start-Up Foundation of Shanghai Jiao Tong University(Grant No.22X010503668).
文摘As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.52275471 and 52175448)Beijing Nova Program No.20220484015the New Cornerstone Science Foundation through the XPLORERPRIZE.
文摘Maintenance of aero-engine fleets is crucial for the efficiency,safety,and reliability of the aviation industry.With the increasing demand for air transportation,maintaining high-performing aero-engines has become significant.Collaborative maintenance,specifically targeting aero-engine fleets,involves the coordination of multiple tasks and resources to enhance management efficiency and reduce costs.Digital Twin(DT)technology provides essential technical support for the intelligent operation and maintenance of aero-engine fleets.DT maps physical object properties to the virtual world,creating high-fidelity,dynamic models.However,DT-enhanced collaborative maintenance faces various challenges,including the construction of complex system-layer DT models,management of massive integrated DT data,and the development of fusion mechanisms and decision-making methods for DT data and models.Overcoming these challenges will allow the aviation industry to optimize aero-engine fleet maintenance,ensuring safety,efficiency,and cost-effectiveness while meeting the growing demand for air transportation.
基金supported in part by the National Major Scientific Research Equipment of China (61927803)the National Natural Science Foundation of China Basic Science Center Project (61988101)+1 种基金Science and Technology Innovation Program of Hunan Province (2021RC4054)the China Postdoctoral Science Foundation (2021M691681)。
文摘The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.
基金supported in part by the National Key Research and Development Program of China(No.2018YFE0177000)the National Natural Science Foundation of China(No.52075257)。
基金Major Science and Technology Project of Anhui Province(Grant Number:201903a05020011)Talents Research Fund Project of Hefei University(Grant Number:20RC14)+2 种基金the Natural Science Research Project of Anhui Universities(Grant Number:KJ2021A0995)Graduate Student Quality Engineering Project of Hefei University(Grant Number:2021Yjyxm09)Enterprise Research Project:Research on Robot Intelligent Magnetic Force Recognition and Diagnosis Technology Based on DT and Deep Learning Optimization.
文摘The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.
