Interacting multiple particle filters for fault diagnosis of non-linear stochastic systems

In this paper, an approach to fault diagnosis in a nonlinear stochastic dynamic system is proposed using the interacting multiple particle filtering (IMPF) algorithm. The fault diagnostic approach is formulated as a hybrid multiple- model estimation scheme. The proposed diagnostic approach provides an integrated framework to estimate the system´s current operational or faulty mode, as well as the unmeasured state variables in the system. Particle filtering algorithm is used to statistically model the underlying dynamics of a nonlinear/non-Gaussian stochastic system. A set of models is assumed to present the possible system behavior pattern or modes. A bank of particle filters runs in parallel, each based on a particular mode, to obtain mode-conditional estimates according to the probabilistically weighting scheme. The interaction among particle filters allows estimation from multiple filters to be fused in a principled manner. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method by comparing it with other nonlinear estimation techniques (extended Kalman filter (EKF) and unscented Kalman filter (UKF)-based).