基于强跟踪滤波的传感器故障诊断的改进方法

Improved method for sensor fault diagnosis based on strong tracking filtering

针对基于强跟踪卡尔曼滤波的传感器故障诊断方法中存在的滤波稳定性差、估计精度低的缺点,提出了双滤波器的方法。一个滤波器的量测噪声方差和系统噪声方差均大于实际值,它对故障的估计精度较低,但跟踪速度较快;另一个滤波器的算法中的量测噪声方差大于实际值,它对故障的估计精度较高,但跟踪速度较慢,正好与前者形成互补,然后用第一个滤波器实现故障的及时检测,用第二个滤波器实现对故障幅值的精确估计。仿真实验表明,该方法较好地兼顾了滤波稳定性、估计精度及速度。

Aiming at the deficiency of bad stability of filter and low precision estimation in the method for sensor fault diagnosis based on strong tracking Kalman filtering,two-filters method is put forward.If the measurement noise variance and system noise variance of a filter are both more than its real noise variance,then its fault estimation precision will be low,but tracking speed will be high;inversely,if only the measurement noise variance of another filter is more than its real noise variance,then its fault estimation precision will be high,and tracking speed will be low.So the first filter can be used to detect the fault timely and the second filter can be used to estimate the fault amplitude accurately.The results of simula-tion experiments show that this method is effective.