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 |
