Determination and Evaluation of the Critical Liquid Nitrogen for Superconducting Levitator Based on Multisensor Fusion Method

Liquid nitrogen (LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>) 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 determination and detection of the critical LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 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 effective method to detect the critical LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content in superconducting levitator. This paper proposes to employ multisensor fusion framework to fuse and enhance the accuracy of critical LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content testing. Four temperature sensors were deployed inside superconducting levitator to measure the temperature change during the LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content changing from 100 % to 0. It was first obtained that the critical LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> content in the superconducting levitator is 4%. To accurately monitor the critical LN<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 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%. This work can provide a preliminary research foundation for the online monitoring and fault diagnosis of HTS maglev train.