Methodologies for uncertainty management in prognostics

Abstract

Effective uncertainty management processes are essential elements in the design of prognostic modules if they to be viable for Integrated Vehicle Health Management (IVHM) systems. Modeling uncertainty, measurement and estimation uncertainties, future load uncertainty, among other factors, all potentially contribute to prognostic uncertainty. This paper analyzes the source of uncertainties in typical IVHM systems and presents a rigorous set of algorithms for uncertainty management that are generic and capable of addressing a variety of uncertainty sources. Specifically, model parameter uncertainty is addressed by a Bayesian-based updating scheme with two variants. One approach utilizes an inner-outer loop Monte Carlo simulation scheme with hyper-parameter adaptation and is intended for off-line applications, while the other particle filtering-based approach can be implemented on-line in real-time. Modeling uncertainty (or model structure uncertainty) is addressed by a Bayesian model selection/fusion method. Effective approaches for handling diagnostic uncertainty and the aggregation of component level uncertainty to system level are also addressed. Select results for the application of particular algorithms are presented.