STDD-Net: a spatio-temporal degradation disentanglement network for RUL prediction of rolling bearings

Abstract Accurate remaining useful life (RUL) prediction of rolling bearings is essential for ensuring the safety and reliability of industrial machinery. However, existing deep learning approaches often struggle to capture nonlinear degradation dynamics and to disentangle evolving spatiotemporal dependencies across multiple degradation modes. To overcome these limitations, this paper proposes a novel spatio-temporal degradation disentanglement network (STDD-Net). The framework incorporates an early degradation point detection mechanism that combines statistical thresholding and CEEMD-based energy–autocorrelation analysis to ensure that only meaningful degradation stages are modeled. Multi-scale nonlinear features extracted via CEEMD and compressed by principal component analysis are then processed by a degradation-oriented feature decomposition module, which explicitly decouples spatial hierarchies and temporal dynamics. The proposed architecture consists of two collaborative branches: one branch performs capsule-based spatial modeling, and the other employs LSTM for temporal sequence modeling. These branches are jointly optimized through an adaptive fusion strategy with uncertainty-aware regression. Extensive experiments on two benchmark datasets demonstrate that STDD-Net consistently outperforms advanced methods, achieving notable reductions in root mean square error and mean absolute error, while ablation studies further verify the essential contributions of each submodule. These results highlight that STDD-Net provides a robust and generalizable solution for RUL forecasting under diverse operating conditions.