Determination and evaluation of the critical liquid nitrogen for superconducting levitator based on a novel temperature-weight coupling measurement device

TL;DR

This paper introduces a novel multisensor fusion approach with a temperature-weighted coupling device to accurately determine the critical liquid nitrogen content in superconducting levitators, enhancing reliability for maglev train applications.

Contribution

It develops a multisensor fusion framework and a matrix-weighted Kalman filter algorithm for precise, real-time detection of critical LN2 levels in superconducting levitators, addressing a gap in cryogenic monitoring technology.

Findings

Critical LN2 content is 4% in the levitator.

Multisensor fusion improves detection accuracy by 5.6%.

The method supports online monitoring and fault diagnosis.

Abstract

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.