Progressive prediction method for failure data with small sample size

WANG Zhi-hua; FU Hui-min; LIU Cheng-rui

Progressive prediction method for failure data with small sample size

1.
Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2.
Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100191, China

基金项目: Supported by Fanzhou Science and Research Foundation for Young Scholars (Grant No.20100511)

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出版历程
- 收稿日期: 2011-03-04
- 修回日期: 2011-05-11
- 刊出日期: 2011-09-28

Progressive prediction method for failure data with small sample size

1.
Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2.
Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100191, China

Funds: Supported by Fanzhou Science and Research Foundation for Young Scholars (Grant No.20100511)

摘要

摘要: The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper. The modeling and estimation procedure, as well as the forecast and confidence limits formula of the progressive auto regressive (PAR) method were discussed in great detail. PAR model not only inherits the simple linear features of auto regressive (AR) model, but also has applicability for nonlinear systems. An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors. Forecasting results of PAR model were compared with auto regressive moving average (ARMA) model, and it can be seen that the PAR method can be considered good and shows a promise for future applications.
- failure data forecast /
- system reliability /
- small sample /
- progressive prediction /
- nonlinear system
Abstract: The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper. The modeling and estimation procedure, as well as the forecast and confidence limits formula of the progressive auto regressive (PAR) method were discussed in great detail. PAR model not only inherits the simple linear features of auto regressive (AR) model, but also has applicability for nonlinear systems. An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors. Forecasting results of PAR model were compared with auto regressive moving average (ARMA) model, and it can be seen that the PAR method can be considered good and shows a promise for future applications.
- failure data forecast /
- system reliability /
- small sample /
- progressive prediction /
- nonlinear system

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参考文献(9)

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