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Model-based Prognostics with Fixed-lag Particle Filters
Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "Miryam Strautkalns",
"@type": "vcard:Contact",
"hasEmail": "mailto:miryam.strautkalns@nasa.gov"
}
|
| description | Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "2009_PHM_ValveFixedLagFilter.pdf",
"format": "PDF",
"mediaType": "application/pdf",
"description": "2009_PHM_ValveFixedLagFilter.pdf",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2009_PHM_ValveFixedLagFilter.pdf"
}
]
|
| identifier | DASHLINK_769 |
| issued | 2013-06-19 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/769/ |
| modified | 2025-03-31 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Model-based Prognostics with Fixed-lag Particle Filters |
