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

Guodong Wang, Zhen He, Li Xue, Qingan Cui, Shanshan Lv and Panpan Zhou

Reliability Engineering and System Safety, 2017, vol. 160, issue C, 114-121

Abstract: Factors which significantly affect product reliability are of great interest to reliability practitioners. This paper proposes a bootstrap-based methodology for identifying significant factors when both location and scale parameters of the smallest extreme value distribution vary over experimental factors. An industrial thermostat experiment is presented, analyzed, and discussed as an illustrative example. The analysis results show that 1) the misspecification of a constant scale parameter may lead to misidentify spurious effects; 2) the important factors identified by different bootstrap methods (i.e., percentile bootstrapping, bias-corrected percentile bootstrapping, and bias-corrected and accelerated percentile bootstrapping) are different; 3) the number of factors affecting 10th percentile lifetime significantly is less than the number of important factors identified at 63.21th percentile.

Keywords: Design of experiments; Censored data; Percentile; Weibull distribution (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:160:y:2017:i:c:p:114-121

DOI: 10.1016/j.ress.2016.12.006

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