## 7 Conclusions  
This study addressed the critical need for accurate prediction of bearing RUL and optimization of LSP parameters for enhanced bearing performance. Predictive maintenance is essential for reducing downtime and maintenance costs in industrial applications, and the existing methods often lack the capability to effectively capture complex degradation patterns in bearing data. To overcome these challenges, the present research integrates advanced machine learning models with optimization techniques, providing a comprehensive and reliable solution. (1) The study developed a transformer-attention model to effectively capture complex patterns in vibration data, and the prediction accuracy RMSE was 0.989. This accuracy is better than that of CNN-LSTM and standard transformer models. (2) A DNN was implemented to predict the extended RUL after LSP remanufacturing, using key parameters such as laser power density, overlapping rate, spot diameter and current RUL. The FFO algorithm was employed to optimize these parameters, and there was a 29.33% improvement in RUL, which is better than other optimization methods. (3) The model's accuracy was validated through FEA, showing a low relative error ranging from 2.5% to 5.8% between predicted and simulated extended RUL. This study shows the key advantages of the proposed approach compared to traditional methods in predicting bearing RUL and optimizing LSP parameters. The transformer-attention model captures complex degradation patterns in vibration data more effectively than conventional models, which enhances prediction accuracy and reliability. Additionally, the DNN-based RUL prediction (optimized with the FFO algorithm) adjusts LSP parameters, including laser power density and overlap rate, to improve bearing life, which achieves more effective parameter tuning than standard strategies. Validation through FEA further supported the model's precision, showing good alignment between the predicted and simulated RUL values. This study has certain limitations for future exploration. (1) The transformer-attention model achieved a high prediction accuracy (RMSE 0.989). However, its effectiveness may be constrained by the current dataset's limited diversity. Broader testing across varied bearing types and degradation patterns in real-world settings could improve its generalizability and robustness. Future research should focus on data diversification and robustness testing to strengthen model adaptability to diverse industrial environments. (2) The DNN model for extended RUL prediction relies heavily on precise input parameters, such as laser power density and overlap rate, which are often challenging to maintain consistently in industrial applications. Variations in these parameters could impact prediction reliability. Future work could address this limitation by incorporating uncertainty modeling to manage parameter variability, which enhances prediction accuracy under fluctuating conditions. Writing--original draft and editing and supervision, Y.L. ; writing--original draft and visualizations, Y.W. ; data analysis, A.L. ; project administration, C.G. and J.T. ; investigation X.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was sponsored by the China Postdoctoral Science Foundation (Project No. 2023M741426) and the open fund project of the National United Engineering Laboratory for Advanced Bearing Tribology (Project No. 202405). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the first authors on request. **Acknowledgments:** This research particularly thanks Zhejiang Nulun Bearing Co., Ltd. for providing experimental support. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Jiangsu Haiyu Machinery Co., Ltd.