[1]龙凤 薛冬林 陈桂明 杨庆.基于粒子滤波与线性自回归的故障预测算法[J].计算机技术与发展,2011,(11):133-136.
　LONG Feng,XUE Dong-lin,CHEN Gui-ming,et al.Fault Prediction Algorithm Based on Particle Filter and Linear Autoregressive Models[J].,2011,(11):133-136.
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基于粒子滤波与线性自回归的故障预测算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年11期
页码:
133-136
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Fault Prediction Algorithm Based on Particle Filter and Linear Autoregressive Models
文章编号:
1673-629X（2011）11-0133-04
作者:
龙凤1 薛冬林2 陈桂明1 杨庆1
[1]第二炮兵工程学院装备管理工程系[2]第二炮兵96604部队
Author(s):
LONG Feng ; XUE Dong-lin ; CHEN Gui-ming;  YANG Qing
[1]Dept. of Equipment Management Eng. ,The Second Artillery Eng. College[2]Troops NO. 96604
关键词:
粒子滤波; 线性自回归模型; 故障预测
Keywords:
particle filter;  linear autoregressive models;  fault prediction
分类号:
TP206
文献标志码:
A
摘要:
粒子退化是粒子滤波在故障预测应用中存在的主要问题。针对粒子滤波算法样本贫化问题，提出一种基于粒子滤波与线性自回归的故障预测算法。在算法的状态估计阶段，使用混合状态系统模型和粒子滤波算法对系统状态的概率密度函数进行估计，并实时给出故障发生概率；在算法的状态预测阶段，采用线性自回归模型对故障征兆随时间的演化情况进行估计及修正，同时给出剩余使用寿命的概率密度函数。故障预测仿真实验结果证明了算法的有效性
Abstract:
Particle degeneracy is the main problem when a particle filter is applied to fault prediction. Focusing on the problem of sample impoverishment of particle filter algorithm, the fault prediction algorithm based on particle filter and finear autoregressive models is proposed, At the state estimation stage, the algorithm uses hybrid system state models and particle filter to estimate probability density function of the system state and support the real-time fault prognosis. At the state prediction stage, the algorithm estimates and corrects the system fault evolution process using linear autoregressive models. Simulation results demonstrate that the fault prediction algorithm based on particle filter and linear autoregressive models is feasible

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备注/Memo

备注/Memo:
军队科研项目（2010066）龙凤（1972-），男，博士研究生，从事机械设备状态监测与故障诊断技术研究；陈桂明，教授，博士生导师，从事机械设备状态监测与故障诊断教学与研究工作
更新日期/Last Update: 1900-01-01