Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content

Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

Metadata Updated: April 11, 2025

Electrolytic capacitors are used in several applications rang- ing from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past expe- riences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress condi- tions during operations. This makes them good candidates for prognostics and health management. Model-based prognos- tics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation mod- els for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.

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.

Downloads & Resources

Dates

Metadata Created Date November 12, 2020
Metadata Updated Date April 11, 2025
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 April 11, 2025
Publisher Dashlink
Maintainer
Identifier DASHLINK_692
Data First Published 2013-04-12
Data Last Modified 2025-04-01
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 0c958d01-c704-43fe-a9f1-25495dab96da
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/692/
Program Code 026:029
Source Datajson Identifier True
Source Hash 021cf7d3eff98939d96a7417d45df4a26f4923584d6ec9b994c0469385d4673f
Source Schema Version 1.1

Didn't find what you're looking for? Suggest a dataset here.