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

fYear

2006

fDate

15-19 May 2006

Firstpage

3807

Lastpage

3812

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1050-4729

Print_ISBN

0-7803-9505-0

Type

conf

DOI

10.1109/ROBOT.2006.1642284

Filename

1642284

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2106956