A fault detection scheme for distributed nonlinear uncertain systems

This paper considers the problem of designing a fault detection scheme for a distributed nonlinear dynamic system. A network of distributed estimators is constructed where an adaptive estimator based on an on-line neural approximation model is embedded into each estimation agent. The local detection decision is made on the basis of the knowledge of the local dynamic model and on an on-line-learned approximation of the dynamic influence of the neighboring sub-systems. The stability of the adaptive estimation scheme is rigorously investigated and sufficient fault detectability conditions are also proposed. Simulation results are finally provided to demonstrate the effectiveness of the proposed architecture and methodology