Model-based fault diagnosis using nonlinear estimators: a neural approach

The problem of model-based fault detection and isolation (FDI) is addressed. An architecture for FDI is devised, exploiting a novel class of nonlinear discrete-time sliding-window observers. Theoretical motivations for such an architecture on the basis of convergence properties of the observers are addressed, and a rather large class of faults are considered, including actuators, sensors, and several kinds of plant malfunctions. Moreover, the use of neural networks is introduced as reliable functional approximators, thus allowing an on-line application of the proposed FDI scheme