Multiple Damage Progression Paths in Model-based Prognostics
Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent wear is driven by several different degradation phenom- ena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics method- ology using particle filters, in which the problem of charac- terizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is repre- sented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model- based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.
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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 | Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent wear is driven by several different degradation phenom- ena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics method- ology using particle filters, in which the problem of charac- terizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is repre- sented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model- based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "2011_IEEEAerospace_pump_paths.pdf",
"format": "PDF",
"mediaType": "application/pdf",
"description": "2011_IEEEAerospace_pump_paths.pdf",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2011_IEEEAerospace_pump_paths.pdf"
}
]
|
| identifier | DASHLINK_782 |
| issued | 2013-06-19 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/782/ |
| modified | 2025-04-01 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Multiple Damage Progression Paths in Model-based Prognostics |
