A Strong Tracking Particle Filter with Application to Fault Prediction
强跟踪粒子滤波算法及其在故障预报中的应用

Particle degeneracy and its poor ability to track saltatory states are two main problems when a particle filter is applied to fault prediction.The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.The extended particle filter can resolve the problem of particle degeneracy to a certain extent but can not track the saltatory state.A strong tracking particle filter is put forward by introducing a strong tracking filter into a particle filter to resolve the above problems,in which the strong tracking filter is used to update particles and produce importance densities.As a result, the problems of particle degeneracy and sample impoverishment are ameliorated,and the tracking ability is improved. Simulation results demonstrate that the strong tracking particle filter is feasible and system fault can be predicted in time and correctly.