为提升航空发动机视情维修的经济性、可靠性，需实现部件健康状态的精准量化评估。针对现有方法评估层次局限、精度不足且物理可解释性弱的现状，提出一种基于向导优化(Engine Physics-Guided Optimization, EPGO)算法的部件退化评估框架。该框架以退化因子高精度提取为目标，通过构建线性影响矩阵，将部件退化因子与多传感器响应的复杂关系转化为可计算的物理引导信号，动态生成“发动机向导”矢量，在复杂参数空间中实现精准、稳定的退化因子提取。为验证方法有效性，以涡轴发动机为研究对象，利用仿真数据与实际飞行数据验证算法核心机理与工程适用性。仿真数据验证结果表明：与传统粒子群优化算法相比，基于向导优化算法提取的部件退化因子平均均方根误差降低36.73%，平均相关系数提升16.26 %。飞行数据验证结果表明，向导优化算法提取的退化因子所呈现的退化趋势，符合部件性能退化的物理机理，展现出向导优化算法在部件退化因子提取中具有良好的工程解释性与鲁棒性。本研究为航空发动机部件退化评估提供了一种高精度、高可靠性的解决方案。

To enhance the economic efficiency and reliability of condition-based maintenance for aero-engines, it is crucial to achieve accurate quantitative assessment of component health. Addressing the current limitations of existing methods—such as constrained assessment levels, insufficient accuracy, and weak physical interpretability—this paper proposes a component degradation as-sessment framework based on the Engine Physics-Guided Optimization (EPGO) algorithm. Aimed at high-precision extraction of degradation factors, the framework transforms the complex relationship between component degradation factors and multi-sensor responses into a computable, physics-guided signal by constructing a linear influence matrix. It dynamically generates an "en-gine-guide" vector to achieve precise and stable extraction of degradation factors within the com-plex parameter space. To validate the effectiveness of the method, a turboshaft engine is selected as the research object, and both simulation data and actual flight data are employed to verify the core mechanism and engineering applicability of the algorithm. Verification results using simula-tion data show that, compared with the traditional Particle Swarm Optimization algorithm, the EPGO-based method reduces the average Root Mean Square Error of extracted component degra-dation factors by 36.73% and increases the average correlation coefficient by 16.26%. Results from flight data indicate that the degradation trends revealed by the factors extracted via the EPGO algorithm align with the physical mechanisms of component performance degradation, demonstrating the algorithm's good engineering interpretability and robustness in degradation fac-tor extraction. This study provides a high-precision and highly reliable solution for aero-engine component degradation assessment.