Title | ||
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Optimal Multi-Objective Burn-In Policy based on Time-Transformed Wiener Degradation Process |
Abstract | ||
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Burn-in is an effective and widely used means to improve product reliability by eliminating weak units before they are distributed in the market. Traditional burn-in that distinguishes weak units by failure during testing is inefficient and incompetent for degradation-failed products in which weak units degrade faster than normal individuals. Hence, the manufacturers have to turn to the degradation-based method. The mean lifetime to failure (MTTF) of a burnt-in population is diminished because of this type of burn-in increases the degradation level of all tested units. Ignoring the impact of burn-in leads to inferior test decisions. This study develops a multi-objective burn-in method that can simultaneously minimize the burn-in cost and maximize the burnt-in population's MTTF. We employ the time-transformed Wiener process with random effects to model the nonlinear degradation path of products and develop a burn-in scheme with two decision variables, namely, test duration and screening cutoff level. Cost expression and lifetime-based optimal objective are analytically developed. The optimal test policy is determined using the multi-objective evolutionary algorithm based on decomposition. A simulation study is conducted to demonstrate the usage and effectiveness of the multi-objective burn-in method. |
Year | DOI | Venue |
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2019 | 10.1109/ACCESS.2019.2918510 | IEEE ACCESS |
Keywords | Field | DocType |
Degradation,Wiener process,burn-in,expectation-maximization algorithm,multi-objective optimization | Mean time between failures,Wiener process,Random effects model,Population,Mathematical optimization,Nonlinear system,Evolutionary algorithm,Computer science,Burn-in,Cutoff,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yi Lyu | 1 | 0 | 4.39 |
Yun Zhang | 2 | 576 | 30.23 |
kairui chen | 3 | 34 | 7.33 |
Chen Ci | 4 | 27 | 6.10 |
Xianxian Zeng | 5 | 0 | 0.68 |