## 1 Introduction  
Rolling bearings are critical components in rotating machinery, and their operational status directly impacts the overall stability of the equipment. According to statistics, mechanical failures caused by rolling bearing faults account for over 30% of total rotating machinery failures, highlighting the importance of research on their reliability. In recent years, with the rapid development of artificial intelligence technologies, data-driven reliability research for rolling bearings has become a key focus domestically and internationally. However, vibration signals from bearing faults typically exhibit periodic or quasi-periodic pulse characteristics. In real-world operating conditions, these signals are susceptible to external interference and equipment noise, leading to a reduced signal-to-noise ratio and making it challenging to extract fault features effectively. Consequently, enhancing fault pulse characteristics through signal preprocessing and achieving reliable performance evaluation and prediction based on multidimensional features represent the core challenges in current research. Although existing research has made certain progress in bearing diagnostics and signal denoising, two critical limitations remain: First, most researchers tend to overlook the inherent limitations of denoising algorithms themselves in terms of denoising effectiveness, leading to suboptimal performance in practical applications. Second, there is insufficient research on how to more effectively utilize denoised signals, making the maximization of processed signal value and improvement of utilization efficiency key directions for future breakthroughs. To address these issues, this study proposes a comprehensive solution for bearing reliability assessment and prediction. In the signal preprocessing stage, an improved denoising method is adopted, utilizing the Ivv algorithm to optimize the cyclic frequency and filter length parameters of CYCBD, significantly enhancing denoising performance. For feature processing, the PaCMAP dimensionality reduction method is introduced to address the high-dimensional nature of bearing feature sets, enabling effective visualization of the feature space. Finally, an AdaBoost-MKRVM-based prediction model is constructed, combining ensemble learning with Relevance Vector Machines to substantially improve prediction accuracy. When applied to the Xi'an Jiaotong University bearing life dataset, the proposed method achieves a mean absolute error (MAE) as low as 0.052, significantly outperforming traditional approaches. This provides a scalable theoretical framework for intelligent operation and health management of rotating machinery. The key innovations of this study are as follows:  
1. [noitemsep,topsep=0 pt]
2. Bearing signal denoising: We propose the first Ivv-CYCBD adaptive denoising framework, which resolves the long-standing challenge of experience-dependent parameter optimization in traditional denoising algorithms. 3. Bearing reliability prediction: A joint framework of PaCMAP and AdaBoost-MKRVM is proposed to provide a new idea for bearing reliability prediction. This study focuses on vibration signal analysis of rolling bearings, with experimental data sourced from publicly available datasets. The current research does not consider the effects of extreme operating conditions (such as ultra-high speeds or cryogenic temperatures). Additionally, the model training relies on historical fault data, and zero-shot prediction for completely new bearings remains an area for future investigation. The structure of this paper is as follows: Section 2 introduces the related work of rolling bearing signal noise reduction, feature extraction, and reliability prediction. Section 3 introduces the overview and failure mechanism of rolling bearings, and Section 4 describes the improved denoising method based on the Ivv-algorithm-enhanced CYCBD algorithm. Section 5 describes feature reduction and visualization methods using PaCMAP.