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

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-29 DOI:10.1109/TNSE.2025.3530158

Chulin Zhou;Shiyou Chen;Chaoyang Wang

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引用次数: 0

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.

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.