Dynamic Reliability Models for Systems of Interacting Components

9877107许多系统由简单组件的复杂子系统组成，或者可以被视为简单组件的复杂子系统，由于组件的相互作用和依赖性，这些组件在系统中的行为可能非常复杂。 这些依赖性可能发生，例如，因为当一些组件发生故障时，应力的动态重新分布发生在其余组件之间。 动态方法是将故障机制纳入模型中，以考虑这种相互作用和依赖关系，以及使用微观（组件）关系来确定宏观（系统）故障。 有几个重要的，但困难的问题，关于可靠性，或故障，在这样的动态环境中的系统模型。 本研究的主要目标是研究以下五个问题：（1）基于故障物理学和假设环境的部件和系统可靠性的适当建模，（2）将部件可靠性信息结合到动态设置中的系统可靠性的确定中，（3）识别引起故障的部件的关键配置，（4）确定复杂系统的近似可靠性模型，这些模型可用于统计推断和数据分析目的，以及（5）将多变量故障机制纳入系统可靠性建模中。 虽然（2）-（5）已被广泛研究的独立组件的相干系统，模型纳入组件之间的依赖关系将产生更现实的复杂系统的故障分析。 这样的模型将直接应用到许多问题的当前重要性，包括，例如，现代复杂材料和其他结构和网络的大量组件的故障，提出的调查机制的复杂系统是重要的，例如，在建模和理解之间的关系的微观层面（组件）和宏观层面（系统）的大小尺度的故障。 将要开发的统计方法和概率模型无疑将有助于更好地理解组件级和系统级故障机制的随机性，以及各种应用中一般系统故障的相关统计推断。 特别应用于预测由复合材料（如碳复合材料）或“系统”（如大型计算机网络）制成的结构的可靠性。 此外，新的，非标准的统计模型，在这项研究中开发的累积损伤的概念的基础上没有得到很好的理解，从观察到的故障数据的采样和推断。 因此，该项目的一个重要目标是研究模型的这些方面。 战术和竞争优势，可以获得，因为这里开发的理论模型应该导致更好的系统可靠性预测和它的含义系统设计和系统维护和检查政策。

9877107Many systems are composed of, or can be viewed as, complex subsystems of simple components whose behavior within the system can be quite complicated due to component interactions and dependencies. These dependencies may occur, for example, because dynamic redistribution of stresses occurs among the remaining components when some components fail. The dynamic approach is to incorporate the mechanism of failure into the model to account for such interactions and dependencies, as well as to use micro-level (component) relationships to determine the macro-level (system) failure. There are several important, but difficult, issues regarding reliability, or failure, models for systems in such dynamic environments. The main objectives of this research are to study the following five issues: (1) appropriate modeling of component and system reliability based on the physics of failure and the assumed environment, (2) incorporating component reliability information into the determination of system reliability in a dynamic setting, (3) identifying critical configurations of components that cause failure, (4) determining approximate reliability models for complex systems that are tractable for statistical inference and data analytic purposes, and (5) incorporating multivariate failure mechanisms into modeling system reliability. While (2)-(5) have been studied extensively for coherent systems of independent components, models incorporating dependencies among components would yield more realistic failure analysis of complex systems. Such models would have direct application to many problems of current importance including, for example, failure of modern complex materials and other structures and of networks of large numbers of components.The proposed investigations of the mechanisms for failure of complex systems are important, for example, in modeling and understanding the relationship between the micro-level (component) and macro-level (system) size scales of failure. The statistical methodology and probability models to be developed will undoubtedly contribute to a better understanding of the random nature of failure mechanisms at both the component level and system level, and the associated statistical Inferences for failure of general systems in various applications. Particular applications are in prediction of the reliability of structures made from composite materials (such as carbon composites) or "systems" such as large computer networks. Also, the new, nonstandard statistical models developed in this study based on cumulative damage concepts are not well understood with respect to sampling and inference from observed failure data. Hence, an important goal of the project is to investigate these aspects of the models. Tactical and competitive advantages can be gained since the theoretical models developed here should lead to better system reliability prediction and its implication to system design and the system maintenance and inspection policies.