Degradation Modeling, Reliability Analysis, and Quality Improvement

退化建模、可靠性分析和质量改进退化数据是非常丰富的可靠性信息源，与分析故障前时间数据相比，它提供了许多优势。该项目将开发一类灵活的模型，用于分析退化数据和相关的可靠性推断。这些结果将为加速试验和可靠性改进试验的设计和分析提供有效的方法。在这项工作中，失效时间被定义为指定退化阈值的水平交叉点（首次通过时间）。项目的第一部分将考虑基于扩散过程的模型，用于分析具有连续样本路径的退化数据。这些模型可以适应各种降解率和形状。它们还会自然地导致各种各样的故障时间分布。反高斯分布起着中心作用，类似于具有恒定风险率的指数分布。第二部分将研究一类基于退化的可修复系统数据模型，它非常类似于具有故障数据的非齐次泊松过程。还将考虑多状态退化模型。这项工作是对传统统计过程控制公式的有趣推广。退化数据可用于提供更多信息的加速测试和可靠性改进研究。将进行加速退化试验设计、可靠性改进实验数据分析和稳健设计研究等几个研究课题。在全球竞争和不断增长的客户期望的推动下，工业一直非常强调质量和可靠性的提高。降低产品开发成本和缩短周期的压力也在持续。在这种环境下，高可靠性产品的设计、开发和制造提出了许多挑战。传统上，可靠性领域的重点是收集和分析故障发生时间数据。高可靠性意味着很少的故障，因此产品和工艺设计的可靠性评估和改进可能非常困难。幸运的是，最近传感和测量技术的进步使得收集与组件、系统和制造过程相关的退化和其他性能测量的大量数据成为可能。然而，缺乏灵活的模型和方法推理一直是一个主要的障碍，广泛使用退化数据的可靠性分析。该项目将开发分析可靠性数据的新模型和方法，并将其用于质量改进。

Proposal ID: DMS-0204247PI: Vijayan NairTitle: Degradation Modeling, Reliability Analysis, and Quality ImprovementAbstractDegradation data are a very rich source of reliability information and offer many advantages over the analysis of time-to-failure data. This project will develop a flexible class of models for analyzing degradation data and related reliability inference. These results will be used to obtain efficient methods for the design and analysis of accelerated tests and reliability improvement experiments. In this work, time-to-failure is defined as the level crossing (first-passage time) of a specified degradation threshold. The first part of the project will consider models based on diffusion processes for analyzing degradation data with continuous sample paths. These models can accommodate a variety of degradation rates and shapes. They also lead naturally to a wide class of time-to-failure distributions. The inverse Gaussian distribution plays a central role, similar to the exponential distribution with constant hazard rates. The second part will study a class of degradation-based models for repairable systems data that is quite analogous to non-homogeneous Poisson processes with failure data. Multi-state degradation models will also be considered. This work is an interesting generalization of the formulation in traditional statistical process control. Degradation data allow for more informative accelerated tests and reliability improvement studies. Several research topics on design of accelerated degradation tests, analysis of data from reliability improvement experiments, and robust design studies will be pursued.There has been tremendous emphasis on quality and reliability improvement in industry, driven by global competition and increasing customer expectations. There is also continued pressure to reduce product development costs and cycle times. Design, development, and manufacturing of highly-reliable products in this environment raise many challenges. The focus within the reliability area has traditionally been on the collection and analysis of time-to-failure data. High reliability implies few failures, so reliability estimation and improvement for product and process design can be extremely difficult. Fortunately, recent advances in sensing and measurement technologies are making it feasible to collect extensive amounts of data on degradation and other performance measures associated with components, systems, and manufacturing processes. However, the lack of flexible models and methods inference has been a major deterrent to the widespread use of degradation data for reliability analysis. This project will develop new models and methods for analyzing reliability data and use them for quality improvement.