Simultaneous Fault Diagnosis and Robust Activation Function Estimation of Linear Multiple-Models Considering the Bounded Modelling Uncertainty

In this paper, the main goal is to design an approach that performs fault detection and isolation in non linear systems. Fault diagnosis is established by regarding system as an interpolation of multiple LTI uncertainty models and not as a single global model. In multiple model framework, the purpose of the paper is the generation of a robust activation function of the representative linear model. The fault diagnosis method presented here is based on a bank of robust and decoupled parity spaces. The proposed method allows detection, isolation and estimation of multiple faults which appear simultaneously or in a sequential way. Robust model selection is obtained by considering the residual vector insensitive to faults, but sensitive to modelling uncertainty. Performances of the method are tested on a simulated example