{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "SCOTT POLL", "hasEmail": "mailto:scott.d.poll@nasa.gov"}, "description": "Electrical power systems play a critical role in spacecraft\r\nand aircraft, and they exhibit a rich variety of failure modes.\r\nThis paper discusses electrical power system fault diagnosis\r\nby means of probabilistic techniques. Speci\u0002cally, we discuss\r\nour development of a diagnostic capability for an electrical\r\npower system testbed, ADAPT, located at NASA Ames.\r\nWe emphasize how we have tackled two challenges, regarding\r\nmodelling and real-time performance, often encountered\r\nwhen developing diagnostic applications. We carefully discuss\r\nour Bayesian network modeling approach for electrical\r\npower systems. To achieve real-time performance, we build\r\non recent theoretically well-founded developments that compile\r\na Bayesian network into an arithmetic circuit. Arithmetic\r\ncircuits have low footprint and are optimized for embedded,\r\nreal-time systems such as spacecraft and aircraft.\r\nWe discuss our probabilistic diagnostic models developed for\r\nADAPT along with successful experimental results.", "distribution": [{"@type": "dcat:Distribution", "description": "2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "2008AAAI_Mengshoel_DiagnosingFaultsElectricalPowerSystems.pdf"}], "identifier": "DASHLINK_865", "issued": "2013-12-18", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/865/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft"}