Fault diagnosis in nonlinear stochastic systems via particle filtering

We consider the problem of detecting malfunctions in the actuators or sensors of systems which can be described by nonlinear/non-Gaussian stochastic state-space models. The basic idea is to estimate the state vector of such models using a sequential Monte Carlo technique known as particle filtering. We present several approaches to detecting faults and pinpointing their location within the system, using either one or a bank of particle filters.