Abstract
Fault detection and isolation for nonlinear stochastic systems is an important and difficult problem, since the noises especially the measurement noises are unavoidable in practice. In this paper, a new robust sequential Monte Carlo filtering approach for nonlinear systems with unknown disturbances is proposed from the recursive Bayesian estimation theory. Based on this new filtering technique, a robust fault detection and isolation strategy for nonlinear stochastic systems is presented, which is also illustrated by simulations on a DTS200 three-tank model.
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