An Efficient Deterministic Approach to Model-based Prediction Uncertainty

Metadata Updated: May 2, 2019

Prognostics 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 sys- tem. Prognostics algorithms 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 trajecto- ries of a system by using the unscented transform. Using this approach, we are not only able to reduce the computa- tional 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 experi- mental data from a battery testbed.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Publisher Dashlink
Unique Identifier DASHLINK_691
Miryam Strautkalns
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Datagov Dedupe Retained 20190501230127
Harvest Object Id 8c15d411-71ef-4692-aff0-4954d180fd75
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2013-04-12
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash ba4d72f900498b59a9e2f83c3b3fcfe579045214
Source Schema Version 1.1

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