Passive robust fault detection using RBF neural modeling based on set membership identification

In this paper, a new passive robust fault detection method is proposed. In virtue of its simple topological structure and universal approximation ability, the RBF neural network is utilized in the system identification for the fault detection. The set membership identification is used to calculate a set of uncertain weights which describes the model uncertainty. This set allows obtaining an adaptive threshold of the residual which is next applied to the robust fault detection. A model structure selection scheme which can delete the redundant hidden nodes is proposed to reduce the conservatism of the uncertain set. A narrower threshold can be generated owing to the contraction of uncertain set and therefore the fault detection sensitivity is increased. Three examples show the satisfying performance of the proposed robust fault detection method.