摘要
针对含有未知输入干扰和噪音的不确定动态系统,使用全阶未知输入观测器(Unknown input observer,UIO)来消除干扰项,实现状态估计,结合Kalman滤波器算法来求解状态反馈矩阵,以使得输出残差信号的协方差最小,从而增强系统对噪声的鲁棒性,实现了一种基于最优未知输入观测器的残差产生器.采用极大似然比(Generalized likelihood ratio,GLR)的方法对残差信号进行评估,通过设定的阈值来提高诊断率.最后以风力发电机组传动系统出现加性传感器故障和乘性传感器故障为例,进行了残差信号的仿真,仿真结果说明了该方法的有效性.
A full-order unknown input observer （UIO） is employed for uncertain dynamic systems with unknown input interference and noise to eliminate the interference and achieve state estimation, combine with the Kalman filter algorithm to solve the state feedback matrix to minimum the covariance of the residual signal, so as to enhance the robustness of the system noise, thus an optimal unknown input observer is achieved as a residual generator. The threshold is designed based on the generalized likelihood ratio （GLR） method to evaluate the residual signals and achieve a high fault detection rate. Finally, the drive train system of the wind turbine with additive sensor faults and multiplicative sensor faults is used as an example. The residual signals are simulated and the results shows the effectiveness of the proposed method.
出处
《自动化学报》
EI
CSCD
北大核心
2013年第8期1225-1230,共6页
Acta Automatica Sinica
基金
国家自然科学基金(61273159
61104024
60904077)
中国博士后科学基金(2012M511752)资助~~
关键词
故障诊断
未知输入观测器
KALMAN滤波器
极大似然比
Fault detection, unknown input observer （UIO）, Kalman filter, generalized likelihood ratio （GLR）
