Fault detection and isolation in non-linear stochastic systems - A combined adaptive Monte Carlo filtering and likelihood ratio approach

This paper presents the development of a new method for solving fault detection and isolation (FDI) problem in general non-linear stochastic systems. In this paper, the faults are modelled as unknown changes in system parameters and adaptive Monte Carlo filtering approach is used in deriving an FDI scheme. Essentially, a set of adaptive Monte Carlo filters are designed based on the augmented system models along with a nominal Monte Carlo filter designed based on the nominal system model. The likelihood functions of the observations are then evaluated using the particles from these ( adaptive) Monte Carlo filters and FDI is eventually achieved via the likelihood ratio test. The simulation results on a highly non-linear system are provided which demonstrates the effectiveness of the proposed method.