A sparrow search algorithm-optimized LSTM framework with EMD denoising for rolling element bearing remaining useful life prediction

Accurate remaining useful life (RUL) prediction of rolling element bearings is essential for implementing predictive maintenance strategies in rotating machinery. However, this task remains challenging due to severe signal noise and the inherently complex, nonlinear nature of bearing degradation processes. To address these limitations, this paper proposes a novel hybrid deep learning framework that synergistically integrates adaptive signal processing, intelligent optimization, and probabilistic sequence modeling. The proposed methodology comprises three key stages. First, Empirical Mode Decomposition (EMD) is applied to denoise raw vibration signals and extract physically meaningful degradation features by adaptively decomposing them into intrinsic mode functions (IMFs). Second, the Sparrow Search Algorithm (SSA) is employed to automatically optimize the critical hyperparameters of a Long Short-Term Memory (LSTM) network, thereby enhancing its capability to capture long-term temporal dependencies inherent in the degradation trajectory. Third, a first-passage-time model based on the inverse Gaussian distribution is introduced to provide probabilistic RUL predictions with quantified uncertainty, extending beyond conventional point estimates. The proposed EMD-SSA-LSTM framework is experimentally validated on the publicly available bearing accelerated life test dataset. Comparative results demonstrate that our approach significantly outperforms benchmark methods, including GA-LSTM and PSO-LSTM, achieving superior prediction accuracy, faster convergence, and enhanced robustness. This work provides a comprehensive and effective solution for data-driven bearing prognostics, contributing to reliable predictive maintenance in industrial applications.