A Dual Particle Filter-Based Fault Diagnosis Scheme for Nonlinear Systems

Abstract

In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the particle filtering scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter to simultaneously estimate the augmented states and parameters. The convergence and stability properties of our proposed dual estimation strategy are shown formally to be guaranteed under certain conditions. The advantage of our developed dual estimation method is justified by handling simultaneously and efficiently both the state and time-varying parameters of a nonlinear system. This is accomplished in the context of a health monitoring scheme that employs a unified approach to fault detection (FD), isolation, and identification in a single framework. The performance capabilities of our proposed FD methodology is demonstrated and evaluated by its application to a gas turbine engine through providing state and parameter estimation objectives under simultaneous and concurrent component fault scenarios. Extensive simulation results are provided to substantiate and justify the superiority of our proposed FD methodology when compared with another well-known alternative diagnostic technique that is available in the literature. ? 1993-2012 IEEE.