Test Resource Allocation in Hierarchical Systems Using Bayesian Networks

Abstract

This paper develops analytical methods for test resource allocation that aid in reducing the uncertainty in the system model prediction for multilevel and multidisciplinary systems. The various component, subsystem, and system-level model predictions; the corresponding inputs and calibration parameters; test data; and model and measurement errors are connected efficiently using a Bayesian network. This provides a unified framework for uncertainty analysis where test data can be integrated along with computational simulations. The Bayesian network is used in an inverse problem where the model parameters of multiple subsystems are calibrated simultaneously. This leads to a decrease in the variance of the model parameters, and hence, in the variance of the overall system performance prediction. An optimization-based procedure is then used for test resource allocation using the Bayesian network, and those tests that can effectively reduce the uncertainty in the system model prediction are identified. The proposed methods are extended to three types of aerospace systems-testing applications: structural dynamics (multilevel), thermally induced vibration/flutter (multidisciplinary), and simplified space telescope mirror (multilevel, multidisciplinary).