Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation
Abstract
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2012
- Accession Number
- ADA588754
Entities
People
- Abhinav Saxena
- Jose R. Celaya
- Kai Goebel
Organizations
- National Aeronautics and Space Administration