Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System

The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this pro...

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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 62; číslo 6; s. 3852 - 3861
Hlavní autoři:	Yin, Shen, zhu, xiangping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Dynamical systems Equations Fault detection genetic algorithm Genetic algorithms Genetics hidden state estimation Industrial electronics intelligent particle filter Mathematical model Non-Gaussian Nonlinear systems Nonlinearity Particle filter Real time real-time fault detection State estimation Strategy Genetic algorithm (GA) particle filter (PF) intelligent particle filter (IPF) hidden state estimation real-time fault detection
ISSN:	0278-0046, 1557-9948
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Abstract	The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.
AbstractList	The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.
Author	Shen Yin Xiangping Zhu
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SubjectTerms	Dynamical systems Equations Fault detection genetic algorithm Genetic algorithms Genetics hidden state estimation Industrial electronics intelligent particle filter Mathematical model Non-Gaussian Nonlinear systems Nonlinearity Particle filter Real time real-time fault detection State estimation Strategy
Title	Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
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