Progressive prediction method for failure data with small sample size
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Progressive prediction method for failure data with small sample size
Progressive prediction method for failure data with small sample size
航空动力学报2011年26卷第9期
作者机构:
1. Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics,Beijing,China,100191
2. Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering,Beijing,China,100191
作者简介:
基金信息:
Supported by Fanzhou Science and Research Foundation for Young Scholars (Grant No.20100511)Supported by Fanzhou Science and Research Foundation for Young Scholars (Grant No.20100511)
DOI:
中图分类号:
纸质出版:2011
稿件说明:
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WANG Zhi-hua, FU Hui-min, LIU Cheng-rui. Progressive prediction method for failure data with small sample size[J]. 航空动力学报, 2011,26(9).
WANG Zhi-hua, FU Hui-min, LIU Cheng-rui. Progressive prediction method for failure data with small sample size[J]. 2011, 26(9).
WANG Zhi-hua, FU Hui-min, LIU Cheng-rui. Progressive prediction method for failure data with small sample size[J]. 航空动力学报, 2011,26(9).DOI:
WANG Zhi-hua, FU Hui-min, LIU Cheng-rui. Progressive prediction method for failure data with small sample size[J]. 2011, 26(9).DOI:
Progressive prediction method for failure data with small sample size
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.
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.