Skip to main content
U.S. flag

An official website of the United States government

A Model-based Prognostics Approach Applied to Pneumatic Valves

Published by Dashlink | National Aeronautics and Space Administration | Catalog Last Checked: November 11, 2026 at 12:41 AM | Dataset Last Updated: March 31, 2025
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Resources

20 resources available

Find Related Datasets

Search by Tags

Click any tag below to search for similar datasets

Complete Metadata
@type dcat:Dataset
accessLevel public
accrualPeriodicity irregular
bureauCode 
[
  "026:00"
]
contactPoint 
{
  "fn": "Scott Goodwin",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:scott.goodwin@nasa.gov"
}
description Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
distribution 
[
  {
    "@type": "dcat:Distribution",
    "title": "Public Distribution File",
    "format": "TXT",
    "mediaType": "text/plain",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_OEM/ISS.OEM_J2K_EPH.txt"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Public Distribution File",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_OEM/ISS.OEM_J2K_EPH.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesINT01",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesINT01.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesINT02",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesINT02.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesINT03",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesINT03.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesINT04",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesINT04.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesINT05",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesINT05.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA01",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA01.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA02",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA02.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA03",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA03.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA04",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA04.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA05",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA05.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA06",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA06.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA07",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA07.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA08",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA08.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA09",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA09.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA10",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA10.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_citiesUSA11",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_citiesUSA11.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_natparksUSA01",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_natparksUSA01.xml"
  },
  {
    "@type": "dcat:Distribution",
    "title": "XMLsightingData_natparksUSA02",
    "format": "XML",
    "mediaType": "application/xml",
    "downloadURL": "https://nasa-public-data.s3.amazonaws.com/iss-coords/2022-01-23/ISS_sightings/XMLsightingData_natparksUSA02.xml"
  }
]
identifier DASHLINK_522
issued 2012-02-07
keyword 
[
  "ames",
  "dashlink",
  "nasa"
]
landingPage https://c3.nasa.gov/dashlink/resources/522/
modified 2025-03-31
programCode 
[
  "026:029"
]
publisher 
{
  "name": "Dashlink",
  "@type": "org:Organization"
}
title A Model-based Prognostics Approach Applied to Pneumatic Valves

data.gov

An official website of the GSA's Technology Transformation Services

Looking for U.S. government information and services?
Visit USA.gov