基于新型温度-权重耦合测量装置的超导悬浮器临界液氮含量测定与评估
Determination and evaluation of the critical liquid nitrogen for superconducting levitator based on a novel temperature-weight coupling measurement device
AI总结：
针对超导悬浮器内液氮含量检测方法缺失的问题，提出多传感器融合框架，采用矩阵加权信息融合Kalman滤波算法，首次测得临界液氮含量为4%，检测精度较单传感器法提升5.6%，为高温超导磁悬浮列车在线监测提供基础。
摘要：
液氮（LN2）是磁悬浮列车的核心部件——超导悬浮器中的高温超导（HTS）块材达到工作状态的唯一冷却介质。临界液氮含量的检测与测定对高温超导磁悬浮列车的可靠运行至关重要。然而，在低温环境与磁悬浮应用的交叉领域中，相关智能检测模型与技术较为匮乏，目前尚无检测超导悬浮器内液氮含量的现有方法。本文提出采用多传感器融合框架来融合并提升临界液氮含量的检测精度。研究在超导悬浮器内部部署了4个温度传感器，测量液氮含量从100%降至0%过程中的温度变化，首次测得超导悬浮器内的临界液氮含量为4%。为精准监测超导悬浮器内的临界液氮含量，研究采用了矩阵加权信息融合Kalman滤波算法。与以往的单传感器方法相比，该多传感器融合方法的检测精度可提升5.6%。本工作可为高温超导磁悬浮列车的在线监测与故障诊断提供初步研究基础。
英文摘要：
Liquid nitrogen (LN2) is the only cooling medium for the high-temperature superconducting (HTS) bulks in the superconducting levitator, which is the heart of the maglev train, to reach working state. The detection and determination of the critical LN2 content are crucial for reliable operation of the HTS maglev train. However, the related intelligent detection model and technology is lack in the combination filed of the cryogenic environment and maglev application, and there is no existing method to detect the LN2 content in superconducting levitator. This paper proposes to employ multisensor fusion framework to fuse and enhance the accuracy of critical LN2 content testing. Four temperature sensors were deployed inside superconducting levitator to measure the temperature change during the LN2 content changing from 100 % to 0. It was first obtained that the critical LN2 content in the superconducting levitator is 4%. To accurately monitor the critical LN2 content in the superconducting levitator, a matrix-weighted information fusion Kalman filter algorithm was used. Compared with the previous single sensor method, the testing accuracy of the multisensor fusion method can be improved by 5.6%. The work can provide a preliminary research foundation for the online monitoring and fault diagnosis of HTS maglev train.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
