Simultaneous Fault Diagnosis and Robust Model Selection in Multiple Linear Models Framework

Abstract

In this paper, the main goal is to design an approach that performs fault detection and isolation in non linear system. Fault diagnosis is established by regarding system as an interpolation of multiple linear time invariant stochastic models and not as a single global model. In multiple model framework, the purpose of the paper consist of generate a robust model selection of the "best" representative linear model. Fault diagnosis method presented here is based on a bank of decoupled Kalman filters. 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 regarding the residual vector insensitive to faults. Performances of the method are tested on a simulation example.