## 1 Introduction  
With the advancement of technology, modern mechanical equipment is evolving towards high-reliability and intelligent systems. Bearings, as critical rotating componentsin mechanical systems, are extensively employed in aerospace applications, heavy machinery, and precision instrumentation. The accurate prediction of bearing remaining useful life (RUL) plays a vital role in industrial applications, effectively mitigating potential catastrophic failures caused by bearing degradation while significantly enhancing system reliability and operational availability [1,2]. Common methods for predicting RUL include probability-statistical approaches and data-driven methods [3]. With advancements in sensor technology and data acquisition capabilities, data-driven RUL prediction methods have become notably more accurate. These methods can automatically infer hidden causal relationships within data, directly extract degradation features from complex systems, effectively process large volumes of monitoring data, and deliver precise RUL predictions [4,5]. Current data-driven prognostic methodologies typically implement vibration signal denoising processing, followed by multi-domain time-frequency feature extraction from the processed signals. Dimensionality reduction algorithms are subsequently employed to fuse heterogeneous features into a unified health index (HI), which is then utilized in prognostic models for estimating RUL [6]. [PERSON] et al. [7] proposed a time-series analytics framework that incorporates multi-scale feature extraction with path-weight selection mechanisms. [PERSON]'s team [8] employed convolutional block attention modules (CBAMs) to dynamically allocate attention weights to degradation-sensitive features. [PERSON] et al. [9] adopted a hybrid architecture that combines long short-term memory (LSTM) networks with Bray-Curtis dissimilarity metrics to quantify mutual information between raw and reconstructed degradation trajectories. [PERSON]'s methodology [10] integrated multi-scale degradation characterization with CNN-GRU hybrid networks for bearing RUL prediction. [PERSON] et al. [11] introduced attention-enhanced gated recurrent units (GRUs) to achieve cross-type bearing lifespan prognostics. [PERSON]'s group [12] developed a modified inverted Transformer architecture with dynamic weighted attention mechanisms to enhance model robustness. Conversely, [PERSON]'s approach [13] focused on temporal dependency modeling through recursive operations but exhibited limitations in capturing spatial features. Effective signal processing and the optimal utilization of monitoring data are critical determinants for achieving accurate predictions of bearing RUL. Conventional signal processing techniques often induce edge blurring and detail smoothing through linear filtering operations, resulting in an irreversible loss of degradation-sensitive information components. This fundamental limitation hinders a comprehensive characterization of bearing degradation states based on measured vibration data. Furthermore, prevailing feature fusion and dimensionality reduction algorithms inevitably incur information dissipation during the mapping from high-dimensional to low-dimensional feature spaces. Given the heterogeneous information content across different feature modalities, current methodologies exhibit insufficient capability in synergistically integrating multi-source degradation signatures to achieve optimal prognostic performance. To address the aforementioned issues, this paper proposes a method for predicting the RUL of rolling bearings based on an improved U-Net model, a multi-dimensional hybrid gated attention mechanism, and a Bi-GRU. Initially, the improved U-Net model is employed to preprocess time-frequency domain signals. Subsequently, a multi-dimensional hybrid gated attention mechanism is introduced, which simultaneously considers both channel global information and spatial information.