## 2 Related Work  
Currently, denoising of rolling bearing vibration signals has become a research hotspot. The primary goal is to remove noise as much as possible while retaining the useful information in the vibration signal. Traditional denoising algorithms include wavelet threshold denoising [1], wavelet packet threshold denoising [2], and Empirical Mode Decomposition (EMD) [3], all of which have shown some effectiveness in noise reduction. Considering the periodic and impulsive characteristics of rolling bearing vibration signals, recent methods such as Minimum Entropy Deconvolution (MED) [4] and Maximum Correlated Kurtosis Deconvolution (MCKD) [5] have been widely applied to vibration signal denoising. [PERSON] et al. [6] proposed a new multi-layer hybrid denoising method that combines Local Mean Decomposition (LMD), Minimum Entropy Deconvolution (MED), and Sparse Coding Shrinkage (SCS) to enhance and extract pulse features, achieving effective noise reduction. To improve fault diagnosis accuracy, [PERSON] et al. [7] developed an improved MCKD-based denoising method, with experimental results demonstrating the superiority of the improved MCKD algorithm in fault diagnosis. With the development of denoising technology, many researchers have recently proposed new methods for noise reduction. [PERSON] et al. [8] introduced a denoising method based on Maximum Cyclostationary Blind Deconvolution (CYCBD). As a novel blind deconvolution technique, CYCBD has shown significant advantages over previous methods, effectively extracting impact features from the noise in rolling bearing vibration signals. [PERSON] et al. [9] applied the CYCBD algorithm to eliminate vibration noise in complex noise environments, significantly enhancing the accuracy of bearing feature extraction. [PERSON] et al. [10] proposed a feature vector selection-based CYCBD algorithm that effectively denoised bearing signals, thereby substantially improving fault feature extraction for rotating machinery. However, in CYCBD, the cyclic frequency and filter length play crucial roles in determining denoising performance. To address this issue, many scholars have proposed improvements to the CYCBD. [PERSON] et al. [11] introduced a cyclic frequency set estimation method based on morphological envelope autocorrelation function, and its effectiveness was validated through simulations and experiments. [PERSON] et al. [12] proposed a method that utilizes the Seagull Optimization Algorithm (SOA) to optimize the optimal filter length in CYCBD. Experimental results demonstrate that this method can effectively extract fault frequencies from signals. The reliability of rolling bearings generally refers to their ability to perform the intended function within a specified timeframe [13]. In evaluating the reliability of rolling bearings, multidimensional features are typically extracted from vibration signals and used for reliability modeling. However, to avoid the curse of dimensionality, it is essential to reduce the dimensionality of these high-dimensional feature sets. Traditional dimensionality reduction algorithms, such as Principal Component Analysis (PCA) [14], Singular Value Decomposition (SVD) [15], and Linear Discriminant Analysis (LDA) [16], have been widely applied in this process. In addition, manifold learning-based dimensionality reduction algorithms have also become increasingly popular for feature reduction. Manifold learning methods usually focus on preserving either the local structure of the data (e.g., t-SNE [17], UMAP [18]) or the global structure (e.g., TriMAP [19]). Although adjusting parameters can balance the retention of both local and global structures, achieving an optimal balance can be challenging. Pairwise Controlled Manifold Approximation (PaCMAP) [20], a dimensionality reduction technique designed for data visualization, uses a dynamic optimization strategy to simultaneously retain both the local and global structures of the original high-dimensional data during dimensionality reduction.