Southwest Jiaotong University

Boyi Zhao

Jun Zheng

Peng Pang

Chenling Xian

Zihan Wang

A method for detecting temperature rise inside a superconducting levitation device based on deep learning is provided. An initial vibration acceleration information, an initial temperature rise information, and a vibration acceleration detection information are obtained. Feature extraction is performed on the initial vibration acceleration information to obtain a high-frequency feature parameter set and a low-frequency feature parameter set. Wavelet band energy calculation is performed for the high-frequency feature parameter set and the low-frequency feature parameter set to obtain a wavelet band energy information. The wavelet band energy information and the initial temperature rise information are input into a preset deep learning network for training to obtain an internal temperature rise detection model of the superconducting levitation device. The vibration acceleration detection information is input into the internal temperature rise detection model to obtain an internal temperature rise prediction information to reflect real-time temperature rise of the superconductor.

Growing community of inventors

Chengdu, China

Boyi Zhao