【摘要】 因恶劣磁场环境激励下超导块材内部温升与悬浮力之间的关系目前仍未可知，所以超导块材内部温升实时监测手段还有待提高。该文搭建了针对超导块材热力学-动力学特性的测试装置，利用Halbach永磁轮产生交变磁场。研究发现，随着永磁轮交变磁场频率的增加，超导块材内部温升呈现逐渐增加的趋势。同时，该文提出了一种结合超导块材温-振特性和BP神经网络（BPNN）的非接触式新型温升测试方法。以逐级加速磁场下的温升-振动数据集为例，利用小波分解提取振动加速度特征，结合BPNN对温升进行识别，准确率可达到99.9%以上。进而研究了超导块材在对极型永磁轨道产生的0.06～0.15 T磁场不平顺激励下的动态特性与内部温升之间的内在关系，识别准确率同样超过99.5%。该文旨在为未来高温超导磁悬浮列车的智能监测和故障诊断提供参考。更多还原

【Abstract】 On January 13, 2021, the first high-temperature superconducting(HTS) pinning high-speed maglev engineering vehicle was launched in Chengdu, China, marking that HTS maglev technology has entered the stage of engineering research. The temperature rise of the HTS bulk in the superconducting levitator and the relevant operation parameters directly determine the safe and stable operation of maglev train. It is urgent to carry out the research of intelligent state recognition theory and test platform to promote the technology development for safe operation of HTS maglev. The temperature rise of the HTS bulk in the superconducting levitator directly determines the levitation performance of the HTS maglev system. However, due to the limitation of the internal structure space inside the superconducting levitator, it is difficult to put in many temperature sensors. On the other hand, although the temperature sensor can directly measure the real-time temperature inside the HTS bulk, it may damage the structure of the HTS bulk. Therefore, a new non-contact temperature rise measurement method combining the thermal-vibration characteristics of the HTS bulk and BP neural network(BPNN) is proposed in this paper, which can achieve a high temperature rise recognition rate. Firstly, a test device for thermal-vibration characteristics of the HTS bulk was built in this paper and an alternating magnetic field(MF) was generated by Halbach permanent magnet(PM) wheel. The temperature rise and dynamic force change of the HTS bulk at 0～4000 r/min were tested experimentally. It was found that the internal temperature rise of the HTS bulk increases progressively with the frequency of the alternating MF generated by the PM wheel. At the same time, based on the temperature rise and vibration data, the vibration acceleration features are extracted by wavelet decomposition, and the temperature rise is identified by BPNN with an accuracy of over 99.9%. Then, to further verify the effectiveness of wavelet decomposition combined with BPNN in temperature identification of the HTS bulk under the excitation of a real permanent magnet guideway(PMG), the thermalvibration characteristics of the HTS bulk were studied in combination with the real MF provided by opposite-pole PMG used in the testing equipment of SCML-03. The thermal-vibration characteristics of the HTS bulk under the abnormal excitation of the PMG were studied by finite element simulation. The intrinsic relationship between the dynamic characteristics of the HTS bulk and the internal temperature rise under the simulated excitation of the actual MF irregularity was studied, and the recognition accuracy was also over 99.5%. The following conclusions can be drawn from the analysis: under the condition of alternating MF provided by PM wheel, the higher the MF change frequency, the higher the internal temperature rise of the HTS bulk, leading to a greater levitation force attenuation. The wavelet energy values of the HTS bulk are different under different fluctuating frequencies of alternating MF, and are different from traditional characteristics such as levitation height. These features can also be used as the main parameters for monitoring the HTS bulk. The non-contact intelligent temperature rise detection method in this paper does not occupy the space inside the superconducting levitator, and is suitable for the dynamic operating conditions of the HTS maglev vehicle, weakening the technical difficulty of the inside of the onboard levitators.更多还原