Degradation Estimation for Distributed Nonlinear Systems: A PDF-Consensus Particle Filtering Method

Abstract

In this article, a distributed probability density function (PDF)-consensus particle filter (PF) algorithm for a class of network systems with degradation is discussed. In order to consider the interaction between component-level degradation and system state, the degradation modelling framework is developed by combining the stochastic degradation process and the state transition model of the system. And the dynamics of the degradation model are described in terms of a Wiener-based process to emphasize the evolution of degradation over time. To reach consensus on the local posterior PDFs at each node in the sense of relative entropy and reduce the communication burden, the state space of the system is divided by a group of weighted grids. Then, the local PDFs are approximated with a combination of the indicator functions and exchange the parameters with neighboring nodes. In order to obtain the particle representation of the fused PDFs, a new importance sampling function is developed to fuse the local grid-based PDFs and to enhance the compatibility of particles with neighboring nodes. A numerical example on target tracking is provided to demonstrate the effectiveness of the proposed scheme.