Abstract

Remaining useful life (RUL) prediction of rolling bearings is essential for ensuring the safe operation and condition-based maintenance of rotating machinery. To address unreliable degradation-onset identification and insufficient joint modeling of trend and detail components in non-stationary degradation signals, this paper proposes a two-stage framework that integrates degradation assessment with the DK-Mamba network. First, a multi-feature health indicator is constructed, and a dual-branch persistent validation strategy (DPV-FPT) is developed to robustly identify the first prediction time (FPT). Second, a multi-scale dynamic decomposition module is employed to adaptively separate the degradation sequence into trend and detail components. The Mamba architecture is then used to capture long-range temporal dependencies in the trend branch, while wavelet-based soft-threshold shrinkage is introduced to suppress noise-like high-frequency disturbances and JacobiKAN is employed to enhance the nonlinear representation of localized impulsive degradation features in the detail branch. Finally, a cross-attention mechanism is employed to adaptively fuse the trend and detail representations for RUL prediction. Experiments on the PHM2012 and IMS datasets demonstrate that the proposed method improves FPT identification reliability and achieves accurate and robust RUL prediction under complex degradation patterns and varying operating conditions.

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