Fault detection and isolation in stochastic non-linear state-space models using particle filters

A novel model-based algorithm for fault detection and isolation (FDI) in stochastic non-linear systems is proposed. The algorithm monitors changes in the process behavior and identifies a corresponding fault using a bank of particle filters running in parallel. The particle filters are used to generate a sequence of hidden states which are then used in a log-likelihood ratio to detect and isolate the faults. The approach is demonstrated through an implementation on two highly nonlinear case studies—a multi-unit chemical reactor system and a polyethylene reactor system. The effectiveness and the robustness of the proposed algorithm are illustrated by comparing the results with FDI techniques that use EKF and UKF state estimators instead of particle filters.