Stochastic observability and fault diagnosis of additive changes in state space models

Author

Gustafsson, Fredrik

Author_Institution

Dept. of Electr. Eng., Linkoping Univ., Sweden

Volume

5

fYear

2001

fDate

2001

Firstpage

2833

Abstract

We derive a Kalman filter based on data from a sliding window. This is used for a new approach to fault detection and diagnosis, where the state estimate from past data is compared to the state estimate of some of the future data. We suggest a method to judge the quality of diagnosis in a simple way. For fault estimation in the diagnosis, the general concept of stochastic observability in linear systems is introduced. Its role in the design step is illustrated on a problem of estimating the true velocity of a car

Keywords

Kalman filters; fault diagnosis; linear systems; observability; parameter estimation; signal processing; state estimation; state-space methods; Kalman filter; additive changes; fault detection; fault diagnosis; fault estimation; linear systems; signal processing problems; sliding window; state estimate; state space models; stochastic observability; Fault detection; Fault diagnosis; Gaussian noise; Linear systems; Observability; Sections; State estimation; State-space methods; Stochastic processes; Yttrium;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on

Conference_Location

Salt Lake City, UT

ISSN

1520-6149

Print_ISBN

0-7803-7041-4

Type

conf

DOI

10.1109/ICASSP.2001.940236

Filename

940236