Abstract: 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.

Abstract: 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.