Degradation Modeling, Reliability Analysis, and Quality Improvement

退化建模、可靠性分析和质量改进

• 批准号: 0204247
• 负责人: Vijayan Nair
• 金额: $ 25.95万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0204247&HistoricalAwards=false
• 关键词: Degradation; Modeling; Reliability; Analysis; Quality

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.

提案ID：DMS-0204247PI：Vijayan Nair标题：降级建模、可靠性分析和质量改进摘要降级数据是非常丰富的可靠性信息来源，与故障时间数据分析相比具有许多优势。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跨越(首次通过时间)。该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。它们还自然而然地导致了一大类故障间隔时间分布。逆高斯分布发挥了核心作用，类似于具有恒定危险率的指数分布。第二部分将研究一类基于退化的可修系统数据模型，它与具有故障数据的非齐次Poisson过程非常相似。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中公式的有趣推广。退化数据允许进行更具信息量的加速测试和可靠性改进研究。课程将围绕加速降级试验设计、可靠性改进试验数据分析和稳健设计研究展开。在全球竞争和客户期望不断提高的推动下，工业中对质量和可靠性改进的重视程度一直很高。降低产品开发成本和周期时间的压力也持续存在。在这种环境下，高可靠性产品的设计、开发和制造带来了许多挑战。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。高可靠性意味着故障很少，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，传感和测量技术的最新进展使收集与部件、系统和制造工艺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断一直是广泛使用退化数据进行可靠性分析的主要障碍。该项目将开发新的模型和方法来分析可靠性数据，并将其用于质量改进。