Model Adaptation for Prognostics in a Particle Filtering Framework

Metadata Updated: November 12, 2020

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.*

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Dashlink
Unique Identifier Unknown
Maintainer
Identifier DASHLINK_757
Data First Published 2013-06-19
Data Last Modified 2020-01-29
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
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Homepage URL https://c3.nasa.gov/dashlink/resources/757/
Program Code 026:029
Source Datajson Identifier True
Source Hash c63afd96ce7d4a9145667b4cc6c526c9407b28fa
Source Schema Version 1.1

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