Advances in Data Combination, Analysis and Collection for System Reliability Assessment

TL;DR

This paper reviews advanced methodologies for assessing system reliability using limited testing, combining multiple data sources, and employing Bayesian networks and flowgraph models, with applications in complex systems like nuclear and infrastructure systems.

Contribution

It introduces novel approaches for integrating diverse data and models for system reliability assessment, including resource allocation and complex system analysis beyond traditional methods.

Findings

Effective combination of multiple data sources improves reliability estimates.

Bayesian networks and flowgraph models enhance analysis of complex systems.

Resource allocation strategies optimize testing and assessment processes.

Abstract

The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory.