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Model-based monitoring and diagnosis of systems with software-extended behavior

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dc.contributor.advisor Brian C. Williams. en_US
dc.contributor.author Mikaelian, Tsoline en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.date.accessioned 2006-03-29T18:45:10Z
dc.date.available 2006-03-29T18:45:10Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/32445
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005. en_US
dc.description Includes bibliographical references (p. 107-112). en_US
dc.description.abstract Model-based diagnosis of devices has largely operated on hardware systems. However, in most complex systems today, such as aerospace vehicles, automobiles and medical devices, hardware is augmented with software functions that influence the system's behavior. As these sophisticated systems are required to perform increasingly ambitious tasks. there is a growing need to ensure their robustness and safety. Prior work introduced probabilistic, hierarchical, constraint automata (PHCA), to allow compact encoding of both hardware and software behavior. The contribution of this thesis is a capability for monitoring and diagnosing software-extended systems in the presence of delayed symptoms, based on the expressive PHCA modeling formalism. Hardware models are extended to include the behavior of associated embedded software, resulting in more comprehensive diagnoses. This work introduces a novel approach that frames diagnosis over a finite time horizon as a soft constraint optimization problem (COP), which is then decomposed into independent subproblems using tree decomposition techniques. There are two advantages to this approach. First, the approach enables finite-horizon diagnosis in the presence of delayed symptoms. Second, the soft COP formulation provides convenient expressivity for encoding the PHCA models and their execution semantics, and enables the use of decomposition-based, efficient optimal constraint solvers. The solutions to the COP correspond to the most likely state trajectories of the software- extended system. en_US
dc.description.abstract (cont.) These state trajectories are enumerated and tracked within the finite receding horizon, as observations and issued commands become available. The diagnostic capability has been implemented and demonstrated on several scenarios from the aerospace and robotic domains, including vision-based rover navigation, the global metrology subsystem of the MIT SPHERES satellites, and models of the NASA New Millennium Earth Observing One (EO-1) spacecraft. en_US
dc.description.statementofresponsibility by Tsoline Mikaelian. en_US
dc.format.extent 112 p. en_US
dc.format.extent 5529163 bytes
dc.format.extent 5535720 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Aeronautics and Astronautics. en_US
dc.title Model-based monitoring and diagnosis of systems with software-extended behavior en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.identifier.oclc 61719706 en_US


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