摘要
提出了复合拟合法建立状态变量模型,该方法应用于建立高维状态变量模型时,具有较高的精度.将健康参数作为增广的状态变量,设计了卡尔曼滤波器,从而可以根据可测参数的偏离量估计得到健康参数.为了减少自适应模型与真实发动机之间的建模误差,在自适应模型中加入神经网络对稳态基点模型进行修正,从而提高了故障诊断系统的置信度.
The composite least-square method was proposed for establishing the state variable model. The method was also applied to develop high dimension state variable model with high accuracy. By taking health parameters as augmented state variables, a Kalman filter was then designed to predict the health parameters from the deviation of measurable parameters. To minimize the modeling errors between the self tuning model and real engine, a neural network was built to modify the steady-state modeling errors, thus improving the confidence level of fault diagnosis system.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2008年第3期580-584,共5页
Journal of Aerospace Power
