Title :
Particle filtering based multiple-model approach to fault diagnosis in nonlinear stochastic systems
Author :
Ping Li ; Kadirkamanathan, Visakan
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
Abstract :
A novel approach to fault diagnosis in nonlinear stochastic systems is proposed. It is based on the particle filtering (PF) algorithm, a Monte Carlo technique based state estimation method, and the multiple model (MM) approach. The simulation results on a univariate model are provided and the fault detection and isolation performance are compared with that using the extended Kalman filter which demonstrate the effectiveness of the proposed approach.
Keywords :
Monte Carlo methods; fault diagnosis; nonlinear systems; particle filtering (numerical methods); reliability theory; state estimation; stochastic systems; Monte Carlo technique; extended Kalman filter; fault detection and isolation performance; fault diagnosis; multiple-model approach; nonlinear stochastic systems; particle filtering algorithm; state estimation method; univariate model; Atmospheric measurements; Kalman filters; Mathematical model; Particle measurements; Solid modeling; Stochastic systems; Statistical approaches to fault diagnosis Nonlinear systems Stochastic systems Particle filters Bayes estimation;
Conference_Titel :
Control Conference (ECC), 2001 European
Conference_Location :
Porto
Print_ISBN :
978-3-9524173-6-2
