{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Ole Mengshoel", "hasEmail": "mailto:ole.j.mengshoel@nasa.gov"}, "description": "One of NASA\u2019s key mission requirements is robust state estimation.  Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure.   Sensor validation techniques address this problem: given a vector of sensor readings, decide whether sensors have failed, therefore producing bad data.  We take in this paper a probabilistic approach, using Bayesian networks, to diagnosis and sensor validation, and investigate several relevant but slightly different Bayesian network queries. We emphasize that on-board inference can be performed on a compiled model, giving fast and predictable execution times. Our results are illustrated using an electrical power system, and we show that a Bayesian network with over 400 nodes can be compiled into an arithmetic circuit that can correctly answer queries in less than 500 microseconds on average.\r\n\r\nReference:\r\n\r\nO. J. Mengshoel, A. Darwiche, and S. Uckun, \"Sensor Validation using Bayesian Networks.\" In Proc. of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08), Los Angeles, CA, 2008.\r\n\r\nBibTex Reference:\r\n\r\n@inproceedings{mengshoel08sensor,\r\n  author    = {Mengshoel, O. J. and Darwiche, A. and Uckun, S.},\r\n  title     = {Sensor Validation using {Bayesian} Networks},\r\n  booktitle     = {Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08)},\r\n  year      = {2008}\r\n}", "distribution": [{"@type": "dcat:Distribution", "description": "Sensor Validation using Bayesian Networks", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/BNiSAIRASv15.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "BNiSAIRASv15.pdf"}], "identifier": "DASHLINK_12", "issued": "2010-09-09", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/12/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Sensor Validation using Bayesian Networks"}