A nonparametric Bayes approach to decide system burn-in time

Burn-in is the preconditioning of assemblies and the accelerated power-on tests performed on equipment subject to temperature, vibration, voltage, radiation, load, corrosion, and humidity. Burn-in techniques are widely applied to integrated circuits (IC) to enhance the component and system reliability. However, reliability prediction by burn-in at the component level, such as the one using the military (e.g., MIL-STD-280A, 756B, 217E [23–25]) and the industrial standards (e.g., the JEDEC standards), is usually not consistent with the field observations. Here, we propose system burn-in, which can remove many of the residual defects left from component and subsystem burn-in (Chien and Kuo [6]). A nonparametric model is considered because 1) the system configuration is usually very complicated, 2) the components in the system have different failure mechanisms, and 3) there is no good model for modeling incompatibility among components and subsystems (Chien and Kuo [5]; Kuo [16]). Since the cost of testing a system is high and, thus, only small samples are available, a Bayesian nonparametric approach is proposed to determine the system burn-in time. A case study using the proposed approach on MCM ASIC's shows that our model can be applied in the cases where 1) the tests and the samples are expensive, and 2) the records of previous generation of the products can provide information on the failure rate of the system under investigation. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 655–671, 1997     

Bolstering components for maximizing system lifetime

This article considers the problem of which component should be “bolstered” or “improved” in order to stochastically maximize the lifetime of a parallel system, series system, or in general, k-out-of-n system. Various ways of bolstering including active redundance, standby redundancy, and burn-in are studied. Also the method of reducing working temperature or stress level according to Arrhenius models is investigated. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 497–509, 1998     

可靠度和费用约束下的加速老炼试验时间优化

为了提高产品实际使用可靠度,减少老炼试验和保修费用,基于效用函数研究了具有早期失效的产品在可靠度和费用约束下的加速老炼试验时间优化方法。考虑产品寿命分布参数的不确定性,利用自助法估计参数的先验分布,然后通过仿真方法获得实际使用可靠度和费用的联合效用函数。以联合效用函数值最大为优化目标建立加速老炼试验时间优化模型,得到最优的加速老炼试验时间。结合对数正态分布产品在温度应力下的加速老炼试验数值示例,阐述上述方法的应用,并对参数进行敏感性分析,结果表明提出的加速老炼试验方法不但能缩短产品的试验时间,还可以最大程度同时满足费用和可靠性的要求。