23 March 1993 Toward a machine-learning framework for acquiring and exploiting monitoring and diagnostic knowledge
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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.
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Stefanos Manganaris, Stefanos Manganaris, Doug H. Fisher, Doug H. Fisher, Deepak Kulkarni, Deepak Kulkarni, } "Toward a machine-learning framework for acquiring and exploiting monitoring and diagnostic knowledge", Proc. SPIE 1963, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, (23 March 1993); doi: 10.1117/12.141727; https://doi.org/10.1117/12.141727
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