Particle Filter-Based Fault Diagnosis of Nonlinear Systems Using a Dual Particle Filter Scheme
It addresses fault diagnosis for nonlinear systems, but the contribution is incremental as it applies existing dual estimation ideas to a specific domain.
The paper develops a dual particle filter scheme for simultaneous state and parameter estimation in nonlinear systems, applied to fault diagnosis of a gas turbine engine. The method demonstrates superior performance over an alternative technique in simulation.
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 (PF) scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter for simultaneously estimating the augmented states and parameters. The convergence and stability of our proposed dual estimation strategy are shown formally to be guaranteed under certain conditions. The ability of our developed dual estimation method is testified to handle simultaneously and efficiently the states and time-varying parameters of a nonlinear system in a context of health monitoring which employs a unified approach to fault detection, isolation and identification is a single algorithm. The performance capabilities of our proposed fault diagnosis methodology is demonstrated and evaluated by its application to a gas turbine engine through accomplishing state and parameter estimation under simultaneous and concurrent component fault scenarios. Extensive simulation results are provided to substantiate and justify the superiority of our proposed fault diagnosis methodology when compared with another well-known alternative diagnostic technique that is available in the literature.