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
收藏
页码：83 / 88
页数：6
相关论文
共 50 条
  • [1] Prediction About Time Series Based on Updated Prediction ARMA Model
    Sun, Youqiang
    Wang, Rujing
    Sun, Bingyu
    Li, Wenbo
    Jiang, Feng
    2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2013, : 680 - 684
  • [2] Small Sample Fault Data Prediction Study Based on Weibull Model
    Wang, Hongpo
    Yang, Ge
    Bai, Linnan
    Yin, Juan
    Li, Qiang
    2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND MECHANICAL AUTOMATION (CSMA), 2015, : 9 - 14
  • [3] Research on failure rate forecasting method based on ARMA model
    Dept. of Systems Engineering, Beijing Univ. of Aeronautics and Astronautics, Beijing 100083, China
    Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron, 2008, 8 (1588-1591): : 1588 - 1591
  • [4] Progressive prediction method for failure data with small sample size
    WANG ZhihuaFU HuiminLIU ChengruiResearch Center of Small Sample TechnologyBeijing University of Aeronautics and AstronauticsBeijing ChinaScience and Technology on Space Intelligent Control LaboratoryBeijing Institute of Control EngineeringBeijing China
    航空动力学报, 2011, 26 (09) : 2049 - 2053
  • [5] Progressive prediction method for failure data with small sample size
    Wang, Zhi-Hua
    Fu, Hui-Min
    Liu, Cheng-Rui
    Hangkong Dongli Xuebao/Journal of Aerospace Power, 2011, 26 (09): : 2049 - 2053
  • [6] Grey Duane model for reliability growth prediction under small sample uncertain failure data
    Liu, Lianyi
    Liu, Sifeng
    Dong, Wenjie
    QUALITY ENGINEERING, 2024, 36 (04) : 790 - 804
  • [7] Research on Prediction of Network Utilization for NCS Based on ARMA Model and RELS
    He, Fang
    Zhang, Hui
    2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 4548 - 4553
  • [8] Research on Life Prediction Based on Wavelet Transform and ARMA Model for Space Relay
    Yu, Qiong
    Qi, Ming
    Wang, Shujuan
    Zhai, Guofu
    ICIEA: 2009 4TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-6, 2009, : 1266 - 1271
  • [9] The Research of ARMA Prediction Model on The Prediction of Railway Track Irregularity State
    Li, Hai-jiao
    Zhong, Yan
    Qin, Xian-guo
    2013 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (ICCSAI 2013), 2013, : 26 - 30
  • [10] Topics' Popularity Prediction based on ARMA Model
    Song, Yichen
    Li, Aiping
    Quan, Yong
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MATHEMATICS AND ARTIFICIAL INTELLIGENCE (ICMAI 2018), 2018, : 68 - 72
12345