论文部分内容阅读
针对发动机上可用传感器个数小于待估计气路健康参数个数时的卡尔曼滤波估计问题,采用一种健康参数线性组合的方法。通过迭代搜索方法优化得到最优变换矩阵,对原健康参数进行线性组合,生成一组维数等于可用传感器个数的调整参数向量,然后采用卡尔曼滤波器对调整参数向量进行估计,最后通过还原变换得到原健康参数的估计值。以涡扇发动机为研究对象的仿真结果表明,在风扇、压气机的效率以及涡轮效率和流量分别或同时蜕化2%到3%时,该方法能够准确估计出所有的健康参数,而估计参数子集法的估计结果有可能出现明显偏差,甚至误判。
Aiming at the Kalman filter estimation problem that the number of available sensors on the engine is less than the number of airway health parameters to be estimated, a method of linear combination of health parameters is adopted. The optimal transformation matrix is obtained through the iterative search method optimization. The original health parameters are linearly combined to generate a set of adjustment parameter vectors whose dimensions are equal to the available sensors. Then, the Kalman filter is used to estimate the adjustment parameter vectors. Finally, Transform to get the original health parameter estimates. The simulation results of a turbofan engine show that the method can accurately estimate all the health parameters when the efficiency of the fan and the compressor and the efficiency and the flow rate of the turbine degenerate by 2% to 3% respectively or simultaneously, while the estimated parameters Set method of estimation results may be significant deviation, or even misjudgment.