Optimal Multi-Objective Burn-In Policy Based on Time-Transformed Wiener Degradation Process

Abstract

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.

Keywords

• Degradation
• Wiener process
• burn-in
• expectation-maximization algorithm
• multi-objective optimization