Title :
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
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;
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-2685-7
DOI :
10.1109/CDC.1995.480696
