Honarmand-Shazilehei Fatemeh, Pariz Naser, Naghibi Sistani Mohammad B
Department of Electrical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran.
ISA Trans. 2020 Dec;107:214-223. doi: 10.1016/j.isatra.2020.08.008. Epub 2020 Aug 8.
Kalman filter and its different variants are commonly used as optimal methods for fault detection in various types of system components. In this paper, a newly introduced type of aforementioned filters, called modal Kalman filter, is extended and utilized in order to estimate the states of nonlinear systems, for sensor fault detection purposes, in a class of nonlinear certain systems. This method, in contrast to the extended Kalman filter, which employs only the linear term of Taylor expansion, retains higher-order terms; as a result, the estimation error will reduce accordingly. Practicality and effectivity of this method, and its superiority over Kalman filter, in terms of accuracy and promptness of sensor fault detection, are also verified with simulation results.
卡尔曼滤波器及其不同变体通常被用作各类系统组件故障检测的最优方法。本文对一种新引入的上述滤波器——模态卡尔曼滤波器进行了扩展和应用，用于在一类非线性确定性系统中估计非线性系统的状态，以实现传感器故障检测。与仅采用泰勒展开线性项的扩展卡尔曼滤波器不同，该方法保留了高阶项；因此，估计误差将相应减小。仿真结果也验证了该方法的实用性和有效性，以及在传感器故障检测的准确性和及时性方面相对于卡尔曼滤波器的优越性。
