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Lessons Learned from using a Livingstone Model to Diagnose a Main Propulsion SystemNASA researchers have demonstrated that qualitative, model-based reasoning can be used for fault detection in a Main Propulsion System (MPS), a complex, continuous system. At the heart of this diagnostic system is Livingstone, a discrete, propositional logic-based inference engine. Livingstone comprises a language for specifying a discrete model of the system and a set of algorithms that use the model to track the system's state. Livingstone uses the model to test assumptions about the state of a component - observations from the system are compared with values predicted by the model. The intent of this paper is to summarize some advantages of Livingstone seen through our modeling experience: for instance, flexibility in modeling, speed and maturity. We also describe some shortcomings we perceived in the implementation of Livingstone, such as modeling continuous dynamics and handling of transients. We list some upcoming enhancements to the next version of Livingstone that may resolve some of the current limitations.
Document ID
20040045151
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Sweet, Adam
(QSS Group, Inc. Moffett Field, CA, United States)
Bajwa, Anupa
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2003
Subject Category
Documentation And Information Science
Meeting Information
Meeting: JANNAF 39th CS/27th APS/21st PSHS/3rd MSS Joint Subcommittee Meeting
Location: Colorado Springs, CO
Country: United States
Start Date: December 1, 2003
End Date: December 5, 2003
Sponsors: NASA Headquarters, Department of the Army, Department of the Navy, Department of the Air Force
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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