Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems

This paper presents the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. The FDI problem is formulated in the multiple model (MM) environment, then by combining the likelihood ratio (LR) test with the PF, a new FDI scheme is developed. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method.