Intelligent refractory materials represent a new generation of high-temperature functional materials that significantly enhance the service performance of traditional refractories in extreme environments through integ...Intelligent refractory materials represent a new generation of high-temperature functional materials that significantly enhance the service performance of traditional refractories in extreme environments through integrated sensing,response,and adaptive mechanisms.A comprehensive overview of intelligent refractory materials was provided,focusing on their classification,preparation techniques,and industrial applications.Firstly,the categories and design principles of intelligent refractory materials are introduced,including self-healing,self-regulating,and self-diagnosing types,which enhance durability and performance under extreme conditions.Subsequently,advanced preparation technologies are discussed,such as 3D printing for complex geometries,nanocomposite engineering for improved mechanical and thermal properties,gradient design for optimized thermal stress resistance and information technology including machine learning,health monitoring,digital twin.Finally,the industrial applications of these materials are highlighted,particularly in steel metallurgy,building materials industry,and energy.It aims to bridge the gap between research advancements and practical implementation,offering insights into future trends in intelligent refractory material development.展开更多
In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An o...In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.展开更多
基金supported by the Natural Science Foundation of Shaanxi Province(No.2023-JC-QN-0615)the National Natural Science Foundation of China(Nos.52272027 and 52372034).
文摘Intelligent refractory materials represent a new generation of high-temperature functional materials that significantly enhance the service performance of traditional refractories in extreme environments through integrated sensing,response,and adaptive mechanisms.A comprehensive overview of intelligent refractory materials was provided,focusing on their classification,preparation techniques,and industrial applications.Firstly,the categories and design principles of intelligent refractory materials are introduced,including self-healing,self-regulating,and self-diagnosing types,which enhance durability and performance under extreme conditions.Subsequently,advanced preparation technologies are discussed,such as 3D printing for complex geometries,nanocomposite engineering for improved mechanical and thermal properties,gradient design for optimized thermal stress resistance and information technology including machine learning,health monitoring,digital twin.Finally,the industrial applications of these materials are highlighted,particularly in steel metallurgy,building materials industry,and energy.It aims to bridge the gap between research advancements and practical implementation,offering insights into future trends in intelligent refractory material development.
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(200705422009)
文摘In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.
