Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5019372 | Reliability Engineering & System Safety | 2017 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Engineering
Mechanical Engineering
Authors
Guodong Wang, Zhen He, Li Xue, Qingan Cui, Shanshan Lv, Panpan Zhou,