Maximum Liklihood Deterministic Particle Filter for State Estimation and Fault Detection in Stochastic Hybrid Systems

Author

Kazem, A. ; Salut, G. ; Lehmann, F.

Author_Institution

LAAS/CNRS, Toulouse

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1196

Lastpage

1201

Abstract

In this paper we present a deterministic particle method for estimating the joint continuous/discrete state (x, i), of a class of stochastic hybrid systems, where the discrete state obeys a Markov chain, while noisy measurements of continuous states are taken. We focus on the problem of fault detection. A turbojet engine system example is used to demonstrate this approach, in fault detection and estimation

Keywords

Markov processes; fault location; maximum likelihood estimation; particle filtering (numerical methods); state estimation; stochastic systems; Markov chain; continuous states; deterministic particle filtering; fault detection; maximum likelihood; noisy measurements; state estimation; stochastic hybrid system; turbojet engine system; Additive white noise; Equations; Fault detection; Filtering; Maximum likelihood estimation; Particle filters; Particle measurements; State estimation; Stochastic systems; Trajectory; deterministic particle filtering; state estimation; stochastic hybrid systems; switching;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies, 2006. ICTTA '06. 2nd

Conference_Location

Damascus

Print_ISBN

0-7803-9521-2

Type

conf

DOI

10.1109/ICTTA.2006.1684546

Filename

1684546