An Efficient Deterministic Approach to Model-based Prediction Uncertainty

Description

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.

Resources

Name Format Description Link
33 2012_PHM_Uncertainty.pdf https://c3.nasa.gov/dashlink/static/media/publication/2012_PHM_Uncertainty.pdf
21 Search results for publications that cite this dataset by its DOI. https://scholar.google.com/scholar?q=10.5067%2FIMPACTS%2FCPL%2FDATA101
21 Files may be downloaded directly to your workstation from this link https://search.earthdata.nasa.gov/portal/ghrc/search?q=cplimpacts&ghrccloud%2Fdata%2F=
33 Applications of Data from the Cloud Physics Lidar https://ams.confex.com/ams/pdfpapers/85877.pdf
21 IMPACTS Field Campaign Collection DOI http://dx.doi.org/10.5067/IMPACTS/DATA101
21 NASA Armstrong Fact Sheet: ER-2 High-Altitude Airborne Science Aircraft https://www.nasa.gov/centers/armstrong/news/FactSheets/FS-046-DFRC.html
33 The guide document contains detailed information about the dataset https://ghrc.nsstc.nasa.gov/pub/fieldCampaigns/impacts/CPL/doc/cplimpacts_dataset.pdf
21 Cloud Physics Lidar: instrument description and initial measurement results https://doi.org/10.1364/AO.41.003725
21 Airborne lidar measurements of aerosol optical properties during SAFARI-2000 https://doi.org/10.1029/2002JD002370
21 Airborne validation of spatial properties measured by the CALIPSO lidar http://dx.doi.org/10.1029/2007JD008768
21 On the spectral dependence of backscatter from cirrus clouds: Assessing CALIOP’s 1064 nm calibration assumptions using cloud physics lidar measurements https://doi.org/10.1029/2009JD013086
21 Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign https://doi.org/10.1175/BAMS-D-20-0246.1
21 Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (Impacts): The 2022 Deployment https://doi.org/10.1109/IGARSS46834.2022.9883693
21 IMPACTS Field Campaign Project Home Page https://ghrc.nsstc.nasa.gov/home/field-campaigns/impacts
21 IMPACTS Field Campaign Micro Article https://ghrc.nsstc.nasa.gov/home/micro-articles/investigation-microphysics-and-precipitation-atlantic-coast-threatening-snowstorms
21 Instructions for citing GHRC data https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data

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  • nasa
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