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A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging

Metadata Updated: December 6, 2023

A remaining useful life prediction methodology for elec- trolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good can- didate for component level prognostics and health manage- ment. Prognostics provides a way to assess remaining use- ful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated ag- ing test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This prelim- inary remaining life prediction algorithm serves as a demon- stration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation pro- gression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognos- tics methods typically used for remaining useful life predic- tions in other applications.

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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 December 6, 2023
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 December 6, 2023
Publisher Dashlink
Maintainer
Identifier DASHLINK_785
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
Harvest Object Id a261c03b-839a-446d-a643-3b9b6f82df4f
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/785/
Program Code 026:029
Source Datajson Identifier True
Source Hash 625839330b2860a6ee579b39690cd7331c8dbf48176cae581bcbf9257d29d84d
Source Schema Version 1.1

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