An Efficient Deterministic Approach to Model-based Prediction Uncertainty EstimationPrognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.
Document ID
20130001691
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Daigle, Matthew J. (NASA Ames Research Center Moffett Field, CA, United States)
Saxena, Abhinav (Stinger Ghaffarian Technologies, Inc. (SGT, Inc.) Moffett Field, CA, United States)
Goebel, Kai (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 27, 2013
Publication Date
September 23, 2012
Subject Category
Quality Assurance And Reliability
Report/Patent Number
ARC-E-DAA-TN5724
Meeting Information
Meeting: Annual Conference of the Prognostics and Health