Model Adaptation for Prognostics in a Particle Filtering Framework

Metadata Updated: August 1, 2018

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*

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

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Dates

Metadata Created Date August 1, 2018
Metadata Updated Date August 1, 2018
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 August 1, 2018
Publisher Dashlink
Unique Identifier DASHLINK_757
Maintainer
Miryam Strautkalns
Maintainer Email
Id {$oid: 56cf5b00a759fdadc44e57ba}
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 0cd43bb5-7a59-4ccd-a608-1f088008996f
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2013-06-19T19:00:13
Homepage URL https://c3.nasa.gov/dashlink/resources/757/
Language en-US
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2013-06-19T19:00:58
Program Code 026:029
Publisher Hierarchy U.S. Government > National Aeronautics and Space Administration > Dashlink
Source Datajson Identifier True
Source Hash 1f434ad52cd290bb674484168bb2fc7c6fe9ddac
Source Schema Version 1.1

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