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
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
An analysis model of the dV /dt capability for a metal–oxide–semiconductor(MOS) controlled thyristor(MCT) is developed. It is shown that, in addition to the P-well resistance reported previously, the existence of th...An analysis model of the dV /dt capability for a metal–oxide–semiconductor(MOS) controlled thyristor(MCT) is developed. It is shown that, in addition to the P-well resistance reported previously, the existence of the OFF-FET channel resistance in the MCT may degrade the dV /dt capability. Lower P-well and N-well dosages in the MCT are useful in getting a lower threshold voltage of OFF-FET and then a higher dV /dt immunity. However, both dosages are restricted by the requirements for the blocking property and the forward conduction capability. Thus, a double variable lateral doping(DVLD) technique is proposed to realize a high dV /dt immunity without any sacrifice in other properties. The accuracy of the developed model is verified by comparing the obtained results with those from simulations. In addition, this DVLD MCT features mask-saving compared with the conventional MCT fabrication process. The excellent device performance, coupled with the simple fabrication, makes the proposed DVLP MCT a promising candidate for capacitor discharge applications.展开更多
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.
基金supported by the National Natural Science Foundation of China(Grant No.U1330114)the Advance Research Program,China(GrantNo.51308030407)the Opening Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory,China(Grant No.ZHD201201)
文摘An analysis model of the dV /dt capability for a metal–oxide–semiconductor(MOS) controlled thyristor(MCT) is developed. It is shown that, in addition to the P-well resistance reported previously, the existence of the OFF-FET channel resistance in the MCT may degrade the dV /dt capability. Lower P-well and N-well dosages in the MCT are useful in getting a lower threshold voltage of OFF-FET and then a higher dV /dt immunity. However, both dosages are restricted by the requirements for the blocking property and the forward conduction capability. Thus, a double variable lateral doping(DVLD) technique is proposed to realize a high dV /dt immunity without any sacrifice in other properties. The accuracy of the developed model is verified by comparing the obtained results with those from simulations. In addition, this DVLD MCT features mask-saving compared with the conventional MCT fabrication process. The excellent device performance, coupled with the simple fabrication, makes the proposed DVLP MCT a promising candidate for capacitor discharge applications.
