Reliability

TL;DR

This volume discusses innovative approaches in reliability assessment for complex systems, emphasizing interdisciplinary methods that integrate diverse information sources to improve decision-making under uncertainty.

Contribution

It introduces new ideas and methodologies for reliability evaluation that combine experimental data, simulations, and expert knowledge for better system assessment.

Findings

Development of integrated reliability assessment methods

Enhanced decision-making tools under uncertainty

Interdisciplinary approaches to complex system evaluation

Abstract

This special volume of Statistical Sciences presents some innovative, if not provocative, ideas in the area of reliability, or perhaps more appropriately named, integrated system assessment. In this age of exponential growth in science, engineering and technology, the capability to evaluate the performance, reliability and safety of complex systems presents new challenges. Today's methodology must respond to the ever-increasing demands for such evaluations to provide key information for decision and policy makers at all levels of government and industry--problems ranging from international security to space exploration. We, the co-editors of this volume and the authors, believe that scientific progress in reliability assessment requires the development of processes, methods and tools that combine diverse information types (e.g., experiments, computer simulations, expert knowledge) from diverse sources (e.g., scientists, engineers, business developers, technology integrators, decision makers) to assess quantitative performance metrics that can aid decision making under uncertainty. These are highly interdisciplinary problems. The principal role of statistical sciences is to bring statistical rigor, thinking and methodology to these problems.