Title :
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
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;
Conference_Titel :
Information and Communication Technologies, 2006. ICTTA '06. 2nd
Conference_Location :
Damascus
Print_ISBN :
0-7803-9521-2
DOI :
10.1109/ICTTA.2006.1684546
