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29 / 2025-05-08 17:19:45
A RUL Prediction Method for UAV Batteries Based on Multilevel Data Processing with Decay Regularzition Stochastic Configuration Network
Stochastic Configuration Network,MultiLevel Signal Processing,RUL Pridiction
4、System Monitoring, Fault Diagnosis and Prediction
全文待审
Zihao Liao / Guizhou University
Shaobo Li / Guizhou University
Zhou Peng / Guizhou University
Yang Lei / Guizhou University
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.
重要日期

  • 会议日期

    08月01日

    2025

    至

    08月04日

    2025

  • 06月15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
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