Robust model-based fault diagnosis using neural nonlinear estimators

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