Modeling Accelerated Degradation Data for Product Reliability Improvement and Warranty Analysis

该基金支持研究开发加速退化测试（ADT）的新方法，包括分析各种高应力环境中的产品或系统退化，以预测产品寿命或系统性能。该研究集中在当前工业中使用的测试方法中存在的差距，并致力于将这些当前方法扩展到更大的问题领域，包括描述制造中更真实的产品退化场景的非标准退化模型。最初的工作是基于真空荧光显示器（VFD），发光二极管和光纤制造的应用问题。VFD性能有助于激励此类模型。从阴极发射的电子用于消除真空中的杂质，VFD的光强度实际上会增加到一定的时间点，然后才会由于老化引起的退化而降低。标准ADT模型无法描述这种现象。 具体的研究工作包括：（1）建立了基于物理退化路径的应力相关退化模型的一般框架;（2）建立了各种复杂结构的失效时间计算公式;（3）建立了基于物理退化路径的应力相关退化模型。（非线性）ADT模型，沿着基于统计回归方法的不确定性估计;（3）将失效时间数据与单独的退化数据集相结合，以改进产品寿命估计;（4）根据与产品开发相关的时间和成本限制，找到最佳的测试程序（根据获得的信息）。 ADT模型包括（非线性）随机系数，以反映测试单元之间的变异性。Bootstrap rescue程序推导出确定的不确定性，因为更简单的方差近似一般不提供这样的随机系数退化模型。与退化数据，建议的产品故障时间分布的估计作为一个关键的质量措施，评估公司的过程改进，在高可靠性产品，并可以帮助公司管理者决定他们的产品保修政策。 如果成功，这项研究的结果可以强烈影响工艺条件的变化，材料的选择，设备的创新，维护计划的修订和其他生产操作的变化。因此，该研究为企业提高运营效率和盈利能力提供了有价值的信息。

This grant supports research for deriving new methods for accelerated degradation testing, or ADT, which involves analyzing product or system degradation in various high stress environments in order to predict product lifetime or system performance. The research concentrates on existing gaps in current test methods used in industry, and works to extend these current methods to a larger domain of problems, including non standard degradation models that describe more realistic product degradation scenarios in manufacturing. Initial work is based on applied problems with vacuum fluorescent displays (VFDs), light-emitting diodes, and fiber optics manufacturing. VFD performance helps to motivate such models. Emitted electrons from its cathode serve to eliminate impurities in the vacuum, and VFD light intensity actually increases up to a certain point of time before it decreases due to age-induced degradation. Standard ADT models cannot characterize this phenomenon. Specific developments of this research include: (1) A general framework for stress dependent degradation models based on physically motivated degradation paths; (2) Building formulas for failure times of various complex (non-linear) ADT models, along with uncertainty estimates based on statistical resampling methods; (3) Combining failure time data with separate sets of degradation data to improve product lifetime estimates; and (4) Finding optimal test procedures (in terms of information gained) based on time and cost constraints associated with product development. The ADT models include (non linear) random coefficients to reflect variability between test units. Bootstrap resampling procedures are derived to ascertain uncertainty because simpler variance approximations are not generally available with such random coefficient degradation models. With degradation data, the proposed estimates of the product failure time distribution serves as a key quality measure for evaluating a company's process improvement in highly reliable products, and can help company managers decide their product warranty policy. If successful, the results of this research can strongly affect process condition changes, material selections, equipment innovations, maintenance schedule revisions and other production operation changes. Thus, the research provides valuable information for companies to improve their operation efficiency and profitability.