报告详情
A RUL Prediction Method for UAV Batteries Based on Multilevel Data Processing with Decay Regularzition Stochastic Configuration Network
编号：28
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更新：2025-06-15 09:08:43 浏览：16次
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报告时间：暂无持续时间
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摘要
This study proposes a high-precision framework for predict
ing Unmanned Aerial Vehicles (UAVs) batteries’ remaining useful life
(RUL). Utilizing a multilevel data processing approach and a decay
regularization-enhanced stochastic configuration network (SCN), this frame
work aims to improve battery health monitoring (BHM) safety and effi-
ciency. The framework is validated using NASA’s UAV battery dataset
and compared against existing techniques, demonstrating notable perfor
mance improvements. Specifically, the study starts with an initial analy
sis of the battery’s key health indicator (HI) using variational modal
decomposition (VMD), followed by a secondary decomposition using
complete ensemble empirical modal decomposition (CEEMDAN) and
signal-to-noise ratio (SNR). Finally, the signals are further processed by
Fast Fourier Transform (FFT) and Power Spectral Density (PSD) anal
ysis and refined by band-pass filters. Performance evaluation across four
datasets (LLF, ULA, LRF, URA) shows that the proposed model outper
forms comparative models in terms of Root Mean Square Error (RMSE)
and Coefficient of Determination (R 2 ) metrics, particularly on the LRF
dataset, achieving an RMSE of 0.054 and an R 2 of 0.8137, indicating
very high prediction accuracy and model fit.
关键词
Stochastic Configuration Network,MultiLevel Signal Processing,RUL Pridiction
报告人
Zihao Liao
博士研究室 Guizhou University稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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