@article{ZHANG2026, 
author = {Runzhi ZHANG and Yilun CAI and Xingjian WANG and Wenbo HE and Jia XU and Rujun JIA},
title = {Degradation prediction method of switching power supply based on EMD-LSTM},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {8},
pages = {2943-2952},
keywords = {switching power supply, degradation prediction, empirical mode decomposition, long short-term memory network, fault injection},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2025.0872},
doi = {10.13700/j.bh.1001-5965.2025.0872},
abstract = {As a crucial energy provider in electronic systems, accurate monitoring and assessment of the health status of switching power supplies are crucial for ensuring efficient system operation. This research suggests a hybrid strategy that combines empirical mode decomposition (EMD) with a long short-term memory (LSTM) network to overcome the shortcomings of both data-driven and classic physics model-based methods in predicting complicated, non-stationary degradation signals. In the proposed method, EMD is employed to decompose degradation signals into multi-scale intrinsic mode functions. The relevant components are then selected and further processed, while the LSTM network captures long-term dependencies and nonlinear temporal dynamics in the time series. This approach enables accurate prediction of degradation trends in switch-mode power supplies. To verify the effectiveness of the proposed algorithm, a degradation simulation test platform for a switch-mode power supply is designed, and the fault injection method is developed. In order to simulate component-level degradation processes realistically, a fault-injection circuit is expressly made to mimic the degradation behavior of important components, especially capacitor degradation within the filtering module of the switch-mode power supply. Based on the constructed degradation experimental setup, experiments are conducted under degradation conditions to systematically validate the effectiveness of the proposed method.}
}