###### Abstract  
The predictive capability of traditional bearing remaining useful life (RUL) prediction models is insufficient, and the prediction networks lack universality, leading to unsatisfactory results in predicting the RUL of bearings, which leads to untimely maintenance decisions and significant economic losses. In order to solve this problem, this study employs Discrete Wavelet Transform (DWT) to denoise vibration signals and extract multi-domain features; the weighted averages of monotonicity, predictability, trendability, and robustness indicators are first ranked for selecting sensitive feature subsets as inputs for RUL prediction, feature fusion is conducted using the Kernel Principal Component Analysis (KPCA) method to obtain the health index (HI) of the bearing, and the failure threshold of the signal is determined based on the 3-sigma principle. An RUL prediction model, which combines Double Bidirectional Long Short-Term Memory (DBiLSTM) with attention mechanism (A-DBiLSTM), is then developed, and the Bayesian approach is used to adaptively search for network hyperparameters. Experiments were conducted using the PHM2012 dataset and the XJTU-SY dataset; the results indicate that the proposed RUL prediction model demonstrates higher predictive performance, exhibits satisfactory performance across different datasets, and possesses good generalization capability and applicability. This method further enhances the predictive capability of bearing RUL estimation. d 2025 15 4441 14  
processes [11; 12], Bayesian methods [13; 14], and machine learning models (e.g., extreme learning machines (ELMs) [15; 16]). In addition, artificial neural networks [17; 18], support vector machine (SVM) [19], independent component analysis (kurtosis, negentropy) [20], convolutional neural networks (CNNs) [21], deep belief networks [22], recurrent neural networks (RNNs) [23], Long Short-Term Memory (LSTM) networks [24], gated recurrent units (GRUs) [25; 26], graph neural networks (GNNs) [27], and hybrid models [28] are widely used. [PERSON] et al. [29] processed the original vibration signals through Wavelet Packet Transform (WPT) and Complete Ensemble Empirical Mode Decomposition (CEEMD). [PERSON] et al. [30] investigated the state-of-the-art methods for bearing vibration signal processing and conducted code demonstrations using software. [PERSON] et al. [31] studied the vibration signal-based condition monitoring, and various monitoring methods for different vibration states have been listed. [PERSON] et al. [32] applied Fast Fourier Transform (FFT) to convert the original time-domain data of bearing faults into the frequency domain, utilized the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the Support Vector Machine (SVM), and conducted denoising processing on the vibration signals. [PERSON] et al. [33] proposed a degradation feature fusion model that incorporates time-domain, frequency-domain, and time-frequency-domain features into monotonic curves and adopted state-space estimators to predict the bearing RUL. [PERSON] et al. [34] predicted bearing RUL by determining precise thresholds. This was based on the incorporation of the Mahalanobis distance into an improved independent component analysis method. [PERSON] et al. [35] used signal entropy to represent degradation features and applied hybrid metrics to determine bearing failure thresholds. [PERSON] et al. [36] determined bearing failure thresholds by filtering signals in different frequency bands and extracting various advanced entropy features and sparsity measures. [PERSON] et al. [37] predicted bearing RUL by combining CNN with Bidirectional LSTM (BiLSTM). [PERSON] et al. [38] proposed a hybrid prediction method combining the digital twin technology with LSTM networks. This method predicts the bearing RUL based on the use of physical and virtual data. [PERSON] et al. [39] predicted bearing RUL by first filtering the underlying signal using a particle filtering algorithm, then combining CNN and LSTM networks. [PERSON] et al.