Title :
Robust model-based fault diagnosis using neural nonlinear estimators
Author :
Alessandri, A. ; Baglietto, M. ; Parisini, T.
Author_Institution :
CNR, Genova, Italy
Abstract :
Robust model-based fault-diagnosis for nonlinear discrete-time systems is addressed, which is based on a novel class of sliding-window state estimators. A rigorous convergence analysis is performed allowing the computation of residual thresholds when modelling errors are present. The use of neural networks is introduced as reliable functional approximators, thus allowing an online application of the proposed robust fault diagnosis scheme
Keywords :
convergence; discrete time systems; fault diagnosis; function approximation; neural nets; nonlinear systems; state estimation; convergence; discrete-time systems; fault-diagnosis; functional approximation; neural networks; nonlinear estimators; nonlinear systems; residual thresholds; state estimation; Actuators; Computerized monitoring; Convergence; Councils; Fault diagnosis; Neural networks; Performance analysis; Robust control; Robustness; Uncertainty;
Conference_Titel :
Decision and Control, 1998. Proceedings of the 37th IEEE Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
0-7803-4394-8
DOI :
10.1109/CDC.1998.760592
