Dynamic Models and Decision Making for Complex Reliability Systems

评估系统可靠性或决策维护或更换政策的主要挑战是，许多系统组成，或可以被视为，复杂的系统的组件（或子系统），其行为在系统内可能是相当复杂的，由于组件的相互作用和依赖性。这一挑战要求模型选择将"故障物理学"考虑纳入模型，将组件可靠性信息整合到系统可靠性模型中，该模型实际模拟这些类型系统的删失，并考虑模型中的不确定性。 决策过程的关键是适当的模型选择（模型选择），以及组件的可靠性信息集成到系统的可靠性评估，在许多情况下，从系统数据中获得的组件信息涉及复杂的审查机制。本计画的主要目的是研究动态负荷分担可靠度模型与模型选择决策。具体而言，该项目的目标是：1。提出了可靠性系统的动态负载分担模型，该模型考虑了“故障物理”因素以及组件或子系统之间的动态相互作用和依赖关系; 2.获得这些载荷分担系统的概率特性，特别是获得系统寿命分布; 3.研究这些系统的数据累积方案，并为这些负荷分担系统开发统计推断程序，该程序也考虑到审查;以及4.当存在多个相互竞争的模型时，在可靠性系统的背景下制定决策策略，特别是通过模型选择或可能的模型平均来进行决策。这对于维护或更换政策的决策也是至关重要的，对于关键系统或设备在关键操作期间试图防止灾难性故障尤其重要。这个拟议项目的优点源于这样一个事实，即动态负载分担可靠性模型将考虑模型组件的相互作用和依赖关系。这些通用的可靠性模型也将是有用的其他复杂系统，如在物理，生物和医学科学。例如，对于在增加的负载下的机械系统（诸如在拉伸负载下的复合材料，其中纤维段是组件，或者在增加的流量下的路由系统，其中节点是组件），负载分担规则描述负载或流量如何从故障组件转移/重新分配到工作组件，并且考虑"故障物理"。所提出的模型有可能综合“故障物理学”和“统计可靠性”的概念，以描述系统的损坏如何导致系统故障。该项目的统计推断方面，包括模型选择和决策部分，将解决与基于这些动态模型的复杂数据估计模型参数有关的重要问题。这个推理问题还没有被广泛处理的可靠性文献，因此，该项目预计将提供这方面的重大进展。 调查的结果预计将影响工程和其他科学提供新的和更现实的模型，系统故障和推理程序的故障预测和维修和更换决策。

The major challenge for assessing system reliability or decision-making regarding maintenance or replacement policies is that many systems are composed of, or can be viewed as, complex systems of components (or of subsystems) whose behavior within the system can be quite complicated due to omponent interactions and dependencies. This challenge requires model selection that incorporates "physics-of-failure" considerations into the model, integrates component reliability information into system reliability models that realistically models the censoring for these types of systems, and accounts for uncertainties in the model. Crucial to the decision making process is the proper model choice (model selection) as well as the integration of component reliability information into the system reliability assessment where in many cases the component information obtained from system data involves complicated censoring mechanisms. The major goal of this proposed project is to study dynamic load-sharing reliability models and decision-making with model selection for such models. Specifically, the aims of this project are: 1. to propose dynamic load-sharing models for reliability systems that incorporate "physics-of-failure" considerations and the dynamic interactions and dependencies among components or subsystems; 2. to obtain probabilistic properties of these load-sharing systems, in particular, to derive the system life distribution; 3. to examine data-accrual schemes for such systems and to develop statistical inference procedures for these load-sharing systems that also account for censoring; and 4. to develop decision-making strategies in the context of reliability systems when there are several competing models, leading in particular to decision-making with model selection or, possibly, model-averaging.The assessment of system reliability requires accurate prediction of system failure. This is also essential to decision making regarding maintenance or replacement policies and is especially important for key systems or equipment in attempting to prevent catastrophic failures during critical operations. The merit of this proposed project emanates from the fact that the dynamic load-sharing reliability models will take into account model component interactions and dependencies. These generic reliability models will also be useful for other complex systems, such as in physical, biological, and medical sciences. For example, for a mechanical system under increasing load, (such as a composite under tensile loading where fiber segments are components or a routing system under increasing traffic, where the nodes are components) the load-sharing rule describes how the load or traffic is transferred/redistributed from failed components to working components and takes into consideration the "physics-of-failure." The proposed models have the potential to synthesize "physics-of-failure" and "statistical reliability" concepts to describe how damage to the system contributes to system failure. The statistical inference aspects of this project, including the model selection and the decision-making portion, will address important problems pertaining to the estimation of model parameters based on complex data for these dynamic models. This inference problem has not been dealt with extensively in the reliability literature, hence this project is expected to provide significant advances on this direction. The results of the investigations are expected to impact engineering and other sciences by providing novel and more realistic models for system failure and inference procedures for prediction of failure and for making maintenance and replacement decisions.