基于多模型简化CDKF的故障诊断方法

针对使用传统卡尔曼滤波器对非线性系统进行故障诊断,估计精度低的问题,提出了一种新的故障诊断方法.该方法结合多模型自适应估计和简化中央差分卡尔曼滤波器的优点,能在线快速地检测出故障,利用中央差分代替了雅可比矩阵的求解,使系统状态估计准确收敛到真实值附近,而且避免了反复求解量测预测方差等一系列繁杂过程.在执行机构不同故障的情况下,通过与其他算法进行诊断对比,结果表明提出的算法在精度上和运行速度上具有明显的优势.

Fault diagnosis approach based on multiple model estimator with simplified CDKF

As the direct application of traditional Kalman filter to fault diagnosis of the nonlinear system usually results to low estimation accuracy, a new fault diagnosis approach was proposed. The method combines the multitude model adaptive estimation and the simplified central difference Kalman filter. Therefore, it achieves on-line fault detection rapidly, and makes the state estimation values converge to real values correctly benefits from the replacement of the Jacobian matrix calculation by central difference transformation. Moreover, the repeated process of solving measurement equations and variances is avoided. In the presents of various actuator faults, the simulation results indicate the effectiveness and rapidity of the proposed algorithm compared with the other filters.