@article{HE2026, 
author = {Wenbo HE and Yilun CAI and Runzhi ZHANG and Jia XU and Xingjian WANG},
title = {State recognition method for switching power supplies based on ResNet-LSTM},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {8},
pages = {2953-2962},
keywords = {switching power supplies, voltage ripple, wavelet transform, ResNet, long short-term memory neural network},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2025.0698},
doi = {10.13700/j.bh.1001-5965.2025.0698},
abstract = {The capacitance value’s degradation level in relation to its original value is first split into four state intervals in order to accomplish state recognition and degradation monitoring of switching power supply. This gives following state recognition jobs a clear labeling basis. A method combining ripple signal feature extraction with a deep learning classification model is proposed to enable rapid identification of the current state. The wavelet transform is applied to decompose the ripple signal at multiple scales, extracting its time-frequency domain feature maps to capture subtle dynamic characteristics during capacitor degradation. In order to categorize and identify feature maps of various states, a deep convolutional neural network model based on feature extraction is built using a residual network (ResNet) with its potent feature representation capabilities and residual learning mechanism. Finally, a ResNet-LSTM model is employed to predict the power supply’s degradation trend, with results demonstrating relatively accurate prediction performance.}
}