Title :
Particle filtering for adaptive sensor fault detection and identification
Author :
Wei, Tao ; Huang, Yufei ; Chen, Philip
Author_Institution :
Dept. of Electr. Eng., Texas Univ., San Antonio, TX
Abstract :
In this paper, we address the problem of adaptive sensor fault identification and validation by particle filtering. The model-based approaches are developed, where the sensor system is modeled by a Markov switch dynamic state-space model. To handle the nonlinearity of the problem, two different particle filters: mixture Kalman filter (MKF) and stochastic M-algorithm (SMA) are proposed. Simulation results are presented to compare the effectiveness and complexity of MKF and SMA methods
Keywords :
Kalman filters; Markov processes; fault diagnosis; identification; particle filtering (numerical methods); sensors; state-space methods; Markov switch dynamic state-space model; adaptive sensor fault detection; fault identification; mixture Kalman filter; particle filtering; stochastic M-algorithm; Adaptive filters; Decision support systems; Electrical fault detection; Fault detection; Fault diagnosis; Filtering; Mathematical model; Power system modeling; Sensor systems; Stochastic processes;
Conference_Titel :
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9505-0
DOI :
10.1109/ROBOT.2006.1642284
