Design Optimization and Reliability Estimation with Incomplete Uncertainty Information

This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive process of manually cataloging and optimization of discrete-time components of a distributed system.

Abstract

<div class="htmlview paragraph">Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available. Finally, we use this information-based reliability assessment to optimize the engine while maximizing the confidence that the design will meet or exceed a pre-specified reliability target.</div>