## 1 Introduction  
Bearings are critical components in different types of industrial machinery, including automotive, aerospace, and manufacturing systems. The performance and lifespan directly influence the overall efficiency and reliability of mechanical systems [1]. Bearings facilitate smooth motion and support loads to have the proper function essential for operational success. Accurate prediction of the remaining useful life (RUL) of bearings is crucial for effective maintenance planning, as it supports organizations to schedule timely interventions and reduce the risk of unexpected failures [2]. Such failures can lead to significant operational disruptions to and costly downtime for equipment [3]. Traditional RUL prediction methods include statistical approaches and simple machine learning models, but the traditional approaches struggle to capture the complex and nonlinear degradation patterns inherent in bearing wear [4, 5]. Therefore, it is essential to predict RUL to extend the remaining life of the bearing system. Based on the remaining life of the bearing system, it is essential to integrate remanufacturing approaches based on RUL prediction. The potential benefits of advanced remanufacturing techniques have not been fully explored in conjunction with RUL assessments [6]. Laser shock peening (LSP) is a surface enhancement technique that can improve the fatigue life of bearings by inducing beneficial compressive residual stresses in the material [7, 8]. This process enhances the mechanical properties of bearings, and it also reduces crack initiation and propagation [9, 10]. However, the connection between RUL predictions and LSP remanufacturing techniques is still rarely researched, and the potential for innovations in predictive maintenance and lifecycle management is limited. To fully utilize the advantages of remanufacturing, it is essential to enhance the accuracy of RUL predictions and optimize maintenance strategies. Deep learning is an effective tool for predicting the RUL of equipment and machinery due to its powerful data processing capabilities. It is capable of processing complex, multi-dimensional datasets during industrial processes. By integrating advanced remanufacturing processes with predictive analytics, engineers can achieve more effective utilization of remanufactured components for improved operational efficiency and reduced costs during the machinery process. Figure 1 shows the bearing remanufacturing research background. In order to address the limitations, this work introduces a novel RUL prediction framework that utilizes a hybrid transformer-attention algorithm developed to support LSP remanufacturing [11]. The transformer-attention algorithm was applied to vibration data collected from bearings operating under various conditions. The model was trained to predict RUL and support LSP remanufacturing process. The predicted RUL was subsequently used to determine the optimal LSP parameters including laser power density, overlapping rate, and spot diameter, and the remanufacturing process was optimized to the specific degradation state of each bearing. This paper proposes a novel solution for predictive maintenance and remanufacturing with the following innovations:  
* Hybrid transformer-attention model for enhanced RUL prediction: The integration of transformer and attention mechanisms is proposed to predict bearing RUL. The transformer extracts relevant historical data points with similar vibration characteristics by comparing the features with standard samples. The attention mechanism focuses on samples based on the significance of the degradation trend so that a more accurate prediction of the bearing remaining life can be achieved;
* LSP parameter optimization based on RUL: A deep neural network (DNN) was used to predict the extended RUL of the bearing after LSP remanufacturing process, and the predictions were utilized by an optimization algorithm to determine the ideal LSP parameters, including laser power density, overlapping rate, and spot diameter.