{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Miryam Strautkalns", "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", "description": "2009_PHM_ValveFixedLagFilter.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2009_PHM_ValveFixedLagFilter.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "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": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Model-based Prognostics with Fixed-lag Particle Filters"}