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《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:

期数:

2017年01期

页码:

20-25

栏目:

出版日期:

2017-02-15

文章信息/Info

Title:

Non-linear Filtering Method and Its Application in Fault Diagnosis

文章编号:

1000-0682（2017）01-0000-00

作者:

张卫峰1; 惠俊军2; 3; 张合新2?

（1.兰州工业学院 土木工程学院，甘肃 兰州 730050；2.第二炮兵工程大学 自动化系，西安 710025；3.?陕西省宝鸡市150信箱11分箱，陕西 宝鸡 721013）

Author(s):

ZHANG Weifeng1;  HUI Junjun2; 3;  ZHANG Hexin2?

(1.Lanzhou Institute of Technology，Gansu Lanzhou 730050，China；2.Department of Automation，The Second Artillery Engineering university，Xi’an 710025，China；3.?Mailbox 150 extension 11，Shanxi Baoji 721013，China)

关键词:

非线性状态估计; 扩展卡尔曼滤波; Unscented卡尔曼滤波; 粒子滤波

Keywords:

nonlinear state estimation; extended Kalman filter(EKF); Unscented Kalman filter(UKF);  particle filter(PF)

分类号:

V271.4;TN713

DOI:

-

文献标志码:

A

摘要:

传统的滤波方法一般基于线性化和高斯假设，在一定程度上影响了滤波精度和非线性系统故障诊断的准确率。该文从“近似非线性”和“近似概率”的方法入手，分析3种常用的非线性滤波算法：扩展卡尔曼滤波器、Unscented卡尔曼滤波器以及粒子滤波器的原理、方法及特点并介绍其在非线性故障诊断中的应用。

Abstract:

Traditional filtering methods are generally based on linearization or Gaussian hypothesis，which may influence the filtering precision and lead to low diagnosis precision to a certain extent. In this paper, from “approximate nonlinearity” and “approximate probability”, the principles, methods, characteristics of three widely used methods for estimation of nonlinear system, i.e., Extended Kalman Filter(EKF),Unscented Kalman Filter(UKF),and Particle Filter(PF) are analyzed, and finally the application in fault diagnosis are introduced.

参考文献/References:

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备注/Memo

备注/Memo:

?收稿日期：2016-10-08 作者简介：张卫峰(1977），男，陕西长安人，硕士研究生，从事自动控制理论教学与研究.

常用功能

更新日期/Last Update: 1900-01-01