Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates

Shared by Miryam Strautkalns, updated on Jul 29, 2013


Author(s) :
A. Saxena, J. Celaya, B. Saha, S. Saha, K. Goebel

Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research in developing newer and more competent methods for URM is gaining momentum.2Beyond initial concepts, more sophisticated prediction distributions are obtained that are not limited to assumptions of Normality and unimodal characteristics. Most prediction algorithms yield non-parametric distributions that are then approximated as known ones for analytical simplicity, especially for performance assessment methods. Although applying the prognostic metrics introduced earlier with their simple definitions has proven useful, a lot of information about the distributions gets thrown away. In this paper, several techniques have been suggested for incorporating information available from Remaining Useful Life (RUL) distributions, while applying the prognostic performance metrics. These approaches offer a convenient and intuitive visualization of algorithm performance with respect to metrics like prediction horizon and α-λ performance, and also quantify the corresponding performance while incorporating the uncertainty information. A variety of options have been shortlisted that could be employed depending on whether the distributions can be approximated to some known form or cannot be parameterized. This paper presents a qualitative analysis on how and when these techniques should be used along with a quantitative comparison on a real application scenario. A particle filter based prognostic framework has been chosen as the candidate algorithm on which to evaluate the performance metrics due to its unique advantages in uncertainty management and flexibility in accommodating non-linear models and non-Gaussian noise. We investigate how performance estimates get affected by choosing different options of integrating the uncertainty estimates. This allows us to identify the advantages and limitations of these techniques and their applicability towards a standardized performance evaluation method.

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