{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Miryam Strautkalns", "hasEmail": "mailto:miryam.strautkalns@nasa.gov"}, "description": "Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the un- derlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model- based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refuel- ing system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evalu- ate its effectiveness and robustness. The approach is demon- strated using historical pneumatic valve data from the refuel- ing system.", "distribution": [{"@type": "dcat:Distribution", "description": "2011_IJPHM_valves.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2011_IJPHM_valves.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "2011_IJPHM_valves.pdf"}], "identifier": "DASHLINK_758", "issued": "2013-06-19", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/758/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "A Model-Based Prognostics Approach Applied to Pneumatic Valves"}