关键词: 损伤预测, 涡轮导向叶片, Monte Carlo仿真, 航空发动机

Abstract: Aircraft engine maintenance is changing from original timing maintenance to condition-based maintenance, which is based on accurate estimate of the damage to the engine parts or the whole engine. Traditional damage prediction based on finite element theory only accurately predicts damage of the parts under nominal conditions, but it is difficult to guarantee the prediction accuracy of the component damage under other non-nominal conditions caused due to the change of external environment, noise. And its complex analysis process is not conducive to real-time operation of airborne. The paper takes thermo-mechanical damage of turbofan engine guide vane for example, establishes the neural network prediction model between the engine operating condition and vane damage, and then improves the prediction accuracy of the model using Monte Carlo simulation. The simulation results show that the error of predicted TMF damage is bellow 0.4%. And the model can be used to real-time prediction of airborne.

Key words: damage prediction, turbine blade, Monte Carlo simulation, aircraft engine