DocumentCode :
1084141
Title :
Process characterization and optimization based on censored data from highly fractionated experiments
Author :
Lu, Jye-Chyi ; Unal, Cemal
Author_Institution :
Dept. of Stat., North Carolina State Univ., Raleigh, NC, USA
Volume :
43
Issue :
1
fYear :
1994
fDate :
3/1/1994 12:00:00 AM
Firstpage :
145
Lastpage :
155
Abstract :
Censored data resulting from life-test of durable products, coupled with complicated structures of screening experiments, makes process characterization very difficult. Existing methods can be inadequate for modeling such data because important effects and factor levels might be identified wrongly. This article presents an expectation-modeling-maximization (EMM) algorithm, where censored data are imputed as pseudo-complete samples and a forward regression is used to compare all main effects and 2-factor interactions for process characterization. Then, the best combination of controllable variables is determined in order to optimize predictions from the final model. A sensitivity study of the selected models, with changes of imputation and parameter estimation methods, shows the importance of using appropriate models and estimation methods in EMM. The author´s analysis of the Specht (1985) heat-exchanger life-test data indicates that E, EG, EH in the wall data and A, K, D, DJ in the corner data are the dominating factors. However, in finding the best process recipe, one might use a model with a few additional terms, which leads to more accurate predictions for better process optimization
Keywords :
heat exchangers; life testing; parameter estimation; production testing; reliability theory; 2-factor interactions; censored data; controllable variables; durable products life testing; expectation-modeling-maximization algorithm; forward regression; heat-exchanger life-test data; highly fractionated experiments; parameter estimation methods; process characterization; process optimization; pseudo-complete samples; screening experiments; sensitivity study; Fractionation; Least squares approximation; Maximum likelihood estimation; Predictive models; Production systems;
fLanguage :
English
Journal_Title :
Reliability, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9529
Type :
jour
DOI :
10.1109/24.285129
Filename :
285129
Link To Document :
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