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Proceedings Paper

Toward a machine-learning framework for acquiring and exploiting monitoring and diagnostic knowledge
Author(s): Stefanos Manganaris; Doug H. Fisher; Deepak Kulkarni
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Paper Abstract

In this paper we address the problem of detecting and diagnosing faults in physical systems, for which neither prior expertise for the task nor suitable system models are available. We propose an architecture that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus of the paper is on the component of the architecture that discovers classes of behaviors with similar characteristics by observing a system in operation. We investigate a characterization of behaviors based on best fitting approximation models. An experimental prototype has been implemented to test it. We present preliminary results in diagnosing faults of the reaction control system of the Space Shuttle. The merits and limitations of the approach are identified and directions for future work are set.

Paper Details

Date Published: 23 March 1993
PDF: 12 pages
Proc. SPIE 1963, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, (23 March 1993); doi: 10.1117/12.141727
Show Author Affiliations
Stefanos Manganaris, Vanderbilt Univ. (United States)
Doug H. Fisher, Vanderbilt Univ. (United States)
Deepak Kulkarni, NASA Ames Research Ctr. (United States)

Published in SPIE Proceedings Vol. 1963:
Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry
Usama M. Fayyad; Ramasamy Uthurusamy, Editor(s)

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