## 9 Discussion  
This study focuses on the reliability evaluation and prediction of rolling bearings, and the effectiveness of the proposed methods has been validated through experimental data. However, several issues remain to be addressed. Future research can explore the following directions:  
1. Signal Denoising Methods: This study improved the denoising performance by enhancing the CYCBD algorithm. Experimental results show that the improved CYCBD algorithm outperforms traditional methods (such as wavelet transform and EMD) in noise suppression and fault feature extraction. Future research can develop more targeted and adaptive denoising methods based on the characteristics of bearing signals to further improve the efficiency and effectiveness of noise reduction. For example, deep learning techniques can be integrated to design end-to-end denoising models.
2. Adaptability to Complex Operating Conditions: The validation data in this study were primarily obtained from laboratory equipment under ideal experimental conditions and relatively stable operating environments. However, actual operating conditions are often complex and variable, especially under varying speed and load conditions, which require further investigation. Future work can explore data collection and model training methods under multiple operating conditions to enhance the applicability and robustness of the methods in real-world industrial environments.
* 3. Model Optimization and Extension: Although the AdaBoost-MKKVM model demonstrated high prediction accuracy in this study, its computational complexity may limit its application in real-time systems. Future research can investigate more efficient model optimization methods, such as hardware-accelerated computing or model compression techniques. Additionally, other machine learning models (e.g., deep learning or reinforcement learning) can be explored for bearing reliability prediction.
* 4. Multi-Sensor Data Fusion: The current study primarily analyzes vibration signals. Future research can integrate multi-sensor data (e.g., temperature, acoustic signals) for fusion analysis to improve the accuracy of fault diagnosis and reliability prediction. Multi-source data fusion can provide a more comprehensive reflection of the bearing's operating state, offering more reliable support for the health monitoring of industrial equipment. Future research should focus on these directions to refine existing approaches and advance the practical application of reliability evaluation and prediction technologies for rolling bearings.  
[PERSON] wrote the manuscript; [PERSON] and [PERSON] performed the experiment and simulation; [PERSON] and [PERSON] processed the experimental data and participated in the revision of the manuscript; J.Q. provided financial support. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Nantong Institute of Technology electronic information master's project (879002), National College Computer Basic Education Teaching Research Project (2024-AFEC-088), Nantong Institute of Technology University-level \"Public Course Education Teaching Reform Research\" project (2024)GG015).  
All data generated or analyzed to support the findings of this study are included within the article.  
The authors declare no conflicts of interest.