Prediction research about small sample failure data based on ARMA model

被引：0

作者：

Huang Jian-guo ^{[1
]}

Luo Hang ^{[1
]}

Long Bing ^{[1
]}

Wang Hou-jun ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Coll Automat Engn, Chengdu 610054, Sichuan Prov, Peoples R China

来源：

IEEE CIRCUITS AND SYSTEMS INTERNATIONAL CONFERENCE ON TESTING AND DIAGNOSIS | 2009年

关键词：

ARMA model; correlation; particle swarm optimization; parameter estimation; maximum likelihood estimation; prediction; small sample;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, conditions and methods of ARMA model's establishment and prediction were detailed analyzed, which were based on correlation characteristics of sample failure data. Because model parameters getting front Moment Estimation (ME) was very rough to small sample, Particle Swarm Optimization (PSO) algorithm was used in Maximum Likelihood Estimation (MLE) to obtain optimal numerical solutions from probability. Actual verification showed that MLE method based on PSO algorithm could make better digital solutions than ME method Further more, prediction and its 0.95 confidence internal based on ARMA model to small sample failure data were described; which made prediction have much high credibility, and the results of prediction might give an important reference to objects' failure development trend.

引用

页码：83 / 88

页数：6

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