Modeling and Analysis of Profiled Reliability Tests Using Computation-Intensive Statistical Methods

该补助金为开发一种创新的研究方法提供资金，以建模和分析复杂的工程可靠性问题。 该项目包括四个研究和教育任务：1）开发新的统计模型，可以将可靠性分析扩展到多应力分布的可靠性试验数据; 2）融合来自各种资源的可靠性信息，包括机械模型，专家意见和现场故障数据，使用贝叶斯方法及其相关的计算工具进行可靠性估计和预测; 3）论证了广义线性模型和贝叶斯分析方法在可靠性试验设计和优化中的优越性; 4）让科学和工程专业的学生，特别是西班牙裔学生，参与主动学习，并在现实世界的应用中使用概率和统计模型。如果成功，该项目将提高对复杂测试计划和多种失效模式的统计可靠性分析的知识，开发一种数据分析方法，该方法将消除许多不切实际的假设，并将先前的工程知识纳入可靠性分析，并促进质量和可靠性研究人员和行业从业人员对计算统计方法的更广泛理解和赞赏。在这项研究中开发的方法可以应用于汽车工业，电子工业，半导体工业，以及许多其他领域的应用超出了工程，如医学和生物科学。此外，该项目将大大提高基础科学和工程教育在一个主要的西班牙裔服务的大学，促进跨学科的工业工程和数学科学的合作研究环境，并有利于少数学生群体谁是传统上代表性不足的科学和工程事业。

This grant provides funding for the development of an innovative research approach to modeling and analysis of complicated engineering reliability problems. The project consists of four research and education tasks - 1) developing new statistical models that can extend reliability analysis to multiple-stress profiled reliability testing data; 2) fusing reliability information from various resources, including mechanistic model, expert opinions, and field failure data, into reliability estimation and prediction using Bayesian methods and their associated computational tools; 3) demonstrating the advantages of using generalized linear models and Bayesian analysis methods for the design and optimization of reliability tests; 4) engaging science and engineering students, particularly Hispanics students, in active learning and using probabilistic and statistical models in real-world applications.If successful, this project will advance the knowledge of statistical reliability analysis of a complex testing plan and multiple failure modes, develop a data analysis method which will eliminate many unrealistic assumptions and incorporate prior engineering knowledge into reliability analysis, and promote a broader understanding and appreciation of computational statistics methods among quality and reliability researchers and industrial practitioners. The methodology developed in this research can apply to the automobile industry, electronic industry, semiconductor industry, and many other applications in the fields beyond engineering, such as medical and biological sciences. In addition, this project will significantly enhance the basic science and engineering education in a major Hispanics-serving university, foster a collaborative research environment across the disciplines of industrial engineering and mathematical science, and benefit a minority student group who is traditionally underrepresented in science and engineering careers.