The reliability of the main bearings in wind turbines is crucial for their safe and stable operation. However, due to their structural complexity and exposure to harsh operating conditions, these bearings experience variable loads and frequent failures, which affect the overall reliability of wind turbines. Traditional finite element methods (FEM) struggle to meet the requirements of real-time condition monitoring and predictive maintenance. To address this, we propose a novel remaining usage life (RUL) prediction method that integrates finite element model (FEM), dimension reduction technique, and damage accumulation modeling (DAM) theory. First, a high-fidelity physics-based FEM is developed to simulate the behavior of double rolling element bearings under specific operating conditions. Then, Proper Orthogonal Decomposition (POD) is applied to extract dominant modal features from stress field simulations, enabling an efficient low-dimensional representation of the high-dimensional physical field. The method combines data-driven machine learning regression techniques for lightweight modeling. Numerical results demonstrate that the proposed method achieves a over 95% reduction in computing time while maintaining a low average error of 0.5% compared to conventional FEM modeling. Finally, RUL prediction based on DAM theory is performed, facilitating real-time condition monitoring and predictive maintenance of wind turbines. This study presents a promising approach for enabling digital twin technology, enhancing smart operation and maintenance of wind turbines.