{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Miryam Strautkalns", "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", "description": "2010_IEEEAerospace_LimitedSensing.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2010_IEEEAerospace_LimitedSensing.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "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": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Model-based Prognostics under Limited Sensing"}