Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

Abstract

The internal temperature rise inside the high-temperature superconducting (HTS) superconductor resulting from irregular magnetic field (MF) above the permanent magnet guideway (PMG) is a major factor contributing to the decline of levitation performance. Real-time monitoring of the temperature rise inside YBCO superconductor is an important issue for the safe operation of the maglev train systems. However, the existing temperature rise testing method involves destructive intrusion less or more, easily affected by strong MF, occupying limited space and sensors prone to detachment. This paper innovatively proposes a non-contact internal temperature rise testing method combining artificial intelligence (AI) methods. Vibration is common signal of a maglev train system, which inspires to establish a fundamental thermal-dynamic levitation force synchronous testing device for YBCO superconductor. Then, a set of temperature rise-vibration dataset exposed to different alternating MF frequencies is created. The wavelet transform is chosen to extract the frequency band energy of vibration, and the backpropagation neural network is used to identify the corresponding temperature rise. The recognition accuracy can reach over 99.9%, which firstly proves the effectiveness of AI algorithms in the thermal-vibration correlation analysis for the HTS maglev system. The results can provide the foundation reference for the intelligent monitoring and fault diagnosis of thermal-dynamics stabilities of HTS maglev train in the future.

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

“…AI techniques like wavelet transform combined with neural networks facilitate the analysis of thermal-vibration correlations. This approach offers a noninvasive method to monitor internal temperature changes in superconductors, crucial for diagnosing system health and preventing failures before they occur [23].…”