Modeling Accelerated Degradation Data for Product Reliability Improvement and Warranty Analysis

对加速退化数据进行建模以提高产品可靠性和保修分析

• 批准号: 0114903
• 负责人: Paul Kvam
• 金额: $ 27.55万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0114903&HistoricalAwards=false
• 关键词: Modeling; Accelerated; Degradation; Data; Product

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.

这笔赠款支持研究开发加速退化测试（ADT）新方法，该方法涉及分析各种高压力环境中的产品或系统退化，以预测产品寿命或系统性能。该研究集中于工业中当前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，包括描述制造中更现实的产品退化场景的非标准退化模型。最初的工作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。从其阴极发射的电子用于消除真空中的杂质，并且 VFD 光强度实际上会增加到某个时间点，然后由于年龄引起的退化而降低。标准 ADT 模型无法描述这种现象。 本研究的具体进展包括：（1）基于物理驱动的退化路径的应力依赖性退化模型的总体框架； (2) 建立各种复杂（非线性）ADT模型的失效时间公式，以及基于统计重采样方法的不确定性估计； (3) 将故障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测试程序（就获得的信息而言）。 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。导出自举重采样程序是为了确定不确定性，因为这种随机系数退化模型通常不提供更简单的方差近似。利用退化数据，对产品故障时间分布的建议估计可以作为评估公司在高可靠性产品方面的流程改进的关键质量衡量标准，并且可以帮助公司经理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、维护计划的修改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。