A Hybrid CEEMDAN-GRU Framework with Cooperative Denoising for Vibration Trend Prediction of Hydropower Units

Accurate vibration prediction is critical for condition monitoring and predictive maintenance of hydropower units, yet it remains challenging due to strong noise interference, severe non-stationarity, and multi-scale coupling characteristics of raw vibration signals. This paper proposes a novel hybrid prediction framework that integrates cooperative denoising, multi-scale signal decomposition, and deep learning-based sequential modeling to achieve high-precision long-term vibration forecasting. First, a two-stage cooperative denoising stategy combining wavelet threshold denoising (WTD) and singular spectrum analysis (SSA) is designed to suppress high-frequency noise while effectively preserving the global trend and critical transient features. Then the denoised signal is decomposed into a set of physically interpretable intrinsic mode functions (IMFs) and a residual component via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), which alleviates mode mixing and improves decomposition completeness. Subsequently, each IMF component is independently predicted using a gated recurrent unit (GRU) network optimized by the Adam algorithm with adaptive learning rate decay, enabling efficient capture of nonlinear temporal dependencies. The proposed framework is validated using 3.5-year real-world vibration data from an lower guide bearing of a pumped-storage hydropower unit. Experimental results demonstrate that the model achieves MAE = 0.3831, RMSE = 0.6964, MAPE = 0.2832%, and R2=0.9911, compared to 0.6666 for the conventional CEEMDAN-GRU model, a 32.45 percentage point increase and a 54% reduction in unexplained variance. Ablation studies and comparative analyses verify the superiority and statistical significance of the cooperative denoising mechanism and the overall hybrid architecture. This work provides a reliable, interpretable, and deployable tool for the condition monitoring and predictive maintenance of hydropower units, supporting proactive operation and reducing unplanned downtime in clean energy systems.