关键词: 航空发动机, 部件退化, 退化评估, 粒子群算法, 健康管理

Abstract:

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 assessment 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 ‘engine-guide’ vector to achieve precise and stable extraction of degradation factors within the complex 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 simulation 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 degradation factors by 36.69% and increases the average correlation coefficient by 29.75%. 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 factor extraction. This study provides a high-precision and highly reliable solution for aero-engine component degradation assessment.

Key words: aero-engine, component degradation, degradation assessment, particle swarm optimization, health management