Comparison of proportional hazards models and neural networks for reliability estimation

doi:10.1023/A:1018525308809

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 期刊論文 > Item 987654321/68605

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题名:	Comparison of proportional hazards models and neural networks for reliability estimation
作者:	Luxhoj, James T.;徐煥智;Shyur, Huan-jyh
贡献者:	淡江大學資訊管理學系
关键词:	Proportional hazards models;neural networks;accelerated life testing
日期:	1997-06-01
上传时间:	2011-10-23 13:18:56 (UTC+8)
摘要:	Because of increased manufacturing competitiveness, new methods for reliability estimation are being developed. Intelligent manufacturing relies upon accurate component and product reliability estimates for determining warranty costs, as well as optimal maintenance, inspection, and replacement schedules. Accelerated life testing is one approach that is used for shortening the life of products or components or hastening their performance degradation with the purpose of obtaining data that may be used to predict device life or performance under normal operating conditions. The proportional hazards (PH) model is a non-parametric multiple regression approach for reliability estimation, in which a baseline hazard function is modified multiplicatively by covariates (i.e. applied stresses). While the PH model is a distribution-free approach, specific assumptions need to be made about the time behavior of the hazard rates. A neural network (NN) is particularly useful in pattern recognition problems that involve capturing and learning complex underlying (but consistent) trends in the data. Neural networks are highly non-linear, and in some cases are capable of producing better approximations than multiple regression. This paper reports on the comparison of PH and NN models for the analysis of time-dependent dielectric breakdown data for a metal-oxide-semiconductor integrated circuit. In this case, the NN model results in a better fit to the data based upon minimizing the mean square error of the predictions when using failure data from an elevated temperature and voltage to predict reliability at a lower temperature and voltage.
關聯:	Journal of intelligent manufacturing 8(3), pp.227-234
DOI:	10.1023/A:1018525308809
显示于类别:	[資訊管理學系暨研究所] 期刊論文

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