Robust failure detection from the generalized likelihood ratio test

Author

Keller, J.Y. ; Summerer, L. ; Darouach, M.

Author_Institution

CNRS, Univ. Henri Poincare-Nancy I, Cosnes-et-Romain, France

Volume

3

fYear

1995

fDate

13-15 Dec 1995

Firstpage

2389

Abstract

The generalized likelihood ratio (GLR) test employs two steps in identification of failures. The first involves the use of Kalman filter for the unfailed system to generate the innovation, or residual sequence. In the second step, by means of description of various hypothesized failure modes, the likelihood ratio is computed and maximized. Comparison of this maximum ratio to a threshold value yields the failure decision (failed or unfailed). The authors extend this strategy for dynamic stochastic systems with unknown inputs

Keywords

Kalman filters; covariance matrices; fault diagnosis; fault location; state estimation; statistical analysis; stochastic systems; uncertain systems; Kalman filter; dynamic stochastic systems; generalized likelihood ratio test; hypothesized failure modes; innovation sequence; residual sequence; robust failure detection; Equations; Fault detection; Filters; Parameter estimation; Recursive estimation; Robustness; State estimation; Stochastic systems; Technological innovation; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1995., Proceedings of the 34th IEEE Conference on

Conference_Location

New Orleans, LA

ISSN

0191-2216

Print_ISBN

0-7803-2685-7

Type

conf

DOI

10.1109/CDC.1995.480696

Filename

480696