Bootstrap analysis of designed experiments for reliability improvement with a non-constant scale parameter

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• 作者：Guodong Wang^a ; ^{gdwang@tju.edu.cn} ; Zhen He^b ; Li Xue^a ; Qingan Cui^c ; Shanshan Lv^b ; Panpan Zhou^b
• 关键词：Design of experiments ; Censored data ; Percentile ; Weibull distribution
• 刊名：Reliability Engineering & System Safety
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：160
• 期：Complete
• 页码：114-121
• 全文大小：677 K
• 卷排序：160

文摘

Product reliability is improved by design of experiments under both scale and location parameters of smallest extreme value distribution vary with experimental factors. A bootstrap-based methodology is proposed to identify important factors which affect 100pth lifetime percentile significantly. Bootstrapping confidence intervals associating experimental factors are obtained by using three bootstrap methods (i.e., percentile bootstrapping, bias-corrected percentile bootstrapping, and bias-corrected and accelerated percentile bootstrapping). The important factors identified by different bootstrap methods are different. The number of factors affecting 10th percentile significantly is less than the number of important factors identified at 63.21th percentile.