Model-based Prognostics under Limited Sensing
Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often needed. However, it is typically difficult to add enough sensors for reliable prognosis, due to system constraints such as cost and weight. Model-based prognostics helps to offset this problem by exploiting domain knowledge about 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. We develop a model-based prognostics methodology using particle filters, and investigate the benefits of a model-based approach when sensor sets are diminished. We apply our approach to a detailed physics- based model of a pneumatic valve, and perform comprehensive simulation experiments to demonstrate the robustness of model-based approaches under limited sensing scenarios using prognostics performance metrics.
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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 | Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often needed. However, it is typically difficult to add enough sensors for reliable prognosis, due to system constraints such as cost and weight. Model-based prognostics helps to offset this problem by exploiting domain knowledge about 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. We develop a model-based prognostics methodology using particle filters, and investigate the benefits of a model-based approach when sensor sets are diminished. We apply our approach to a detailed physics- based model of a pneumatic valve, and perform comprehensive simulation experiments to demonstrate the robustness of model-based approaches under limited sensing scenarios using prognostics performance metrics. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "2010_IEEEAerospace_LimitedSensing.pdf",
"format": "PDF",
"mediaType": "application/pdf",
"description": "2010_IEEEAerospace_LimitedSensing.pdf",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2010_IEEEAerospace_LimitedSensing.pdf"
}
]
|
| identifier | DASHLINK_771 |
| issued | 2013-06-19 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/771/ |
| modified | 2025-03-31 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Model-based Prognostics under Limited Sensing |
