Dynamic Models and Decision Making for Complex Reliability Systems

University South Carolina Research Foundation, Columbia SC

Investigators

Abstract

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.