【作者】 何文波； 刘士荣； 李文磊；

【Author】 He Wenbo ,Liu Shirong, Li Wenlei Faculty of Information Science and Technology, Ningbo University, Ningbo 315211 School of Automation, Hangzhou Dianzi University, Hangzhou 310018

【机构】 宁波大学信息科学与工程学院； 杭州电子科技大学自动化学院；

【摘要】 研究了基于扩展卡尔曼滤波(EKF)、Unscented卡尔曼滤波(UKF)和粒子滤波器(PF)的非线性随机系统的故障检测与诊断。采用了多模型故障检测,并运用似然比检验方法进行故障模式的分类。在不同噪声环境下,对基于 EKF、UKF和PF的非线性随机系统的故障诊断方法进行了比较研究。仿真结果证明了基于PF方法对于非线性、非高斯系统的故障检测与诊断的有效性。更多还原

【Abstract】 Three methods for fault diagnosis in nonlinear stochastic systems are studied in this paper, which are based on extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Multiple-model based fault detector and fault classification method with the likelihood ratio test are applied to fault diagnosis of nonlinear systems. In various noise environments, the comparative studies have been carried out on the fault detection and diagnosis for nonlinear stochastic systems with EKF, UKF and PF, respectively. Simulations demonstrate the effectiveness of the fault diagnosis method based on the PF in nonlinear and non-Gauassian stochastic systems.更多还原